text:
Cluster analysis
|Machine learning and
data mining
|Dimensionality reduction
|Structured prediction
|Anomaly detection
|This section needs additional citations for verification. (May 2012)
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.
Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.
Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.
Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939[1][2] and famously used by Cattell beginning in 1943[3] for trait theory classification in personality psychology.
- 1 Clusters and clusterings
- 2 Clustering algorithms
- 3 Evaluation of clustering results
- 4 Clustering axioms
- 5 Applications
- 6 See also
- 7 References
- 8 External links
Clusters and clusterings[edit]
According to Vladimir Estivill-Castro, the notion of a "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms.[4] There is a common denominator: a group of data objects. However, different researchers employ different cluster models, and for each of these cluster models again different algorithms can be given. The notion of a cluster, as found by different algorithms, varies significantly in its properties. Understanding these "cluster models" is key to understanding the differences between the various algorithms. Typical cluster models include:
- Connectivity models: for example hierarchical clustering builds models based on distance connectivity.
- Centroid models: for example the k-means algorithm represents each cluster by a single mean vector.
- Distribution models: clusters are modeled using statistical distributions, such as multivariate normal distributions used by the Expectation-maximization algorithm.
- Density models: for example DBSCAN and OPTICS defines clusters as connected dense regions in the data space.
- Subspace models: in Biclustering (also known as Co-clustering or two-mode-clustering), clusters are modeled with both cluster members and relevant attributes.
- Group models: some algorithms do not provide a refined model for their results and just provide the grouping information.
- Graph-based models: a clique, i.e., a subset of nodes in a graph such that every two nodes in the subset are connected by an edge can be considered as a prototypical form of cluster. Relaxations of the complete connectivity requirement (a fraction of the edges can be missing) are known as quasi-cliques.
A "clustering" is essentially a set of such clusters, usually containing all objects in the data set. Additionally, it may specify the relationship of the clusters to each other, for example a hierarchy of clusters embedded in each other. Clusterings can be roughly distinguished as:
- hard clustering: each object belongs to a cluster or not
- soft clustering (also: fuzzy clustering): each object belongs to each cluster to a certain degree (e.g. a likelihood of belonging to the cluster)
There are also finer distinctions possible, for example:
- strict partitioning clustering: here each object belongs to exactly one cluster
- strict partitioning clustering with outliers: objects can also belong to no cluster, and are considered outliers.
- overlapping clustering (also: alternative clustering, multi-view clustering): while usually a hard clustering, objects may belong to more than one cluster.
- hierarchical clustering: objects that belong to a child cluster also belong to the parent cluster
- subspace clustering: while an overlapping clustering, within a uniquely defined subspace, clusters are not expected to overlap.
Clustering algorithms[edit]
Clustering algorithms can be categorized based on their cluster model, as listed above. The following overview will only list the most prominent examples of clustering algorithms, as there are possibly over 100 published clustering algorithms. Not all provide models for their clusters and can thus not easily be categorized. An overview of algorithms explained in Wikipedia can be found in the list of statistics algorithms.
There is no objectively "correct" clustering algorithm, but as it was noted, "clustering is in the eye of the beholder."[4] The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally, unless there is a mathematical reason to prefer one cluster model over another. It should be noted that an algorithm that is designed for one kind of model has no chance on a data set that contains a radically different kind of model.[4] For example, k-means cannot find non-convex clusters.[4]
Connectivity based clustering (hierarchical clustering)[edit]
Connectivity based clustering, also known as hierarchical clustering, is based on the core idea of objects being more related to nearby objects than to objects farther away. These algorithms connect "objects" to form "clusters" based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of the cluster. At different distances, different clusters will form, which can be represented using a dendrogram, which explains where the common name "hierarchical clustering" comes from: these algorithms do not provide a single partitioning of the data set, but instead provide an extensive hierarchy of clusters that merge with each other at certain distances. In a dendrogram, the y-axis marks the distance at which the clusters merge, while the objects are placed along the x-axis such that the clusters don't mix.
Connectivity based clustering is a whole family of methods that differ by the way distances are computed. Apart from the usual choice of distance functions, the user also needs to decide on the linkage criterion (since a cluster consists of multiple objects, there are multiple candidates to compute the distance to) to use. Popular choices are known as single-linkage clustering (the minimum of object distances), complete linkage clustering (the maximum of object distances) or UPGMA ("Unweighted Pair Group Method with Arithmetic Mean", also known as average linkage clustering). Furthermore, hierarchical clustering can be agglomerative (starting with single elements and aggregating them into clusters) or divisive (starting with the complete data set and dividing it into partitions).
These methods will not produce a unique partitioning of the data set, but a hierarchy from which the user still needs to choose appropriate clusters. They are not very robust towards outliers, which will either show up as additional clusters or even cause other clusters to merge (known as "chaining phenomenon", in particular with single-linkage clustering). In the general case, the complexity is , which makes them too slow for large data sets. For some special cases, optimal efficient methods (of complexity ) are known: SLINK[5] for single-linkage and CLINK[6] for complete-linkage clustering. In the data mining community these methods are recognized as a theoretical foundation of cluster analysis, but often considered obsolete. They did however provide inspiration for many later methods such as density based clustering.
Centroid-based clustering[edit]
In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set. When the number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem: find the cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized.
The optimization problem itself is known to be NP-hard, and thus the common approach is to search only for approximate solutions. A particularly well known approximative method is Lloyd's algorithm,[7] often actually referred to as "k-means algorithm". It does however only find a local optimum, and is commonly run multiple times with different random initializations. Variations of k-means often include such optimizations as choosing the best of multiple runs, but also restricting the centroids to members of the data set (k-medoids), choosing medians (k-medians clustering), choosing the initial centers less randomly (K-means++) or allowing a fuzzy cluster assignment (Fuzzy c-means).
Most k-means-type algorithms require the number of clusters - - to be specified in advance, which is considered to be one of the biggest drawbacks of these algorithms. Furthermore, the algorithms prefer clusters of approximately similar size, as they will always assign an object to the nearest centroid. This often leads to incorrectly cut borders in between of clusters (which is not surprising, as the algorithm optimized cluster centers, not cluster borders).
K-means has a number of interesting theoretical properties. On the one hand, it partitions the data space into a structure known as a Voronoi diagram. On the other hand, it is conceptually close to nearest neighbor classification, and as such is popular in machine learning. Third, it can be seen as a variation of model based classification, and Lloyd's algorithm as a variation of the Expectation-maximization algorithm for this model discussed below.
Distribution-based clustering[edit]
The clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A nice property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution.
While the theoretical foundation of these methods is excellent, they suffer from one key problem known as overfitting, unless constraints are put on the model complexity. A more complex model will usually always be able to explain the data better, which makes choosing the appropriate model complexity inherently difficult.
One prominent method is known as Gaussian mixture models (using the expectation-maximization algorithm). Here, the data set is usually modelled with a fixed (to avoid overfitting) number of Gaussian distributions that are initialized randomly and whose parameters are iteratively optimized to fit better to the data set. This will converge to a local optimum, so multiple runs may produce different results. In order to obtain a hard clustering, objects are often then assigned to the Gaussian distribution they most likely belong to; for soft clusterings, this is not necessary.
Distribution-based clustering is a semantically strong[clarification needed] method, as it not only provides you with clusters, but also produces complex models for the clusters that can also capture correlation and dependence of attributes. However, using these algorithms puts an extra burden on the user: to choose appropriate data models to optimize, and for many real data sets, there may be no mathematical model available the algorithm is able to optimize (e.g. assuming Gaussian distributions is a rather strong assumption on the data).
Density-based clustering[edit]
In density-based clustering,[8] clusters are defined as areas of higher density than the remainder of the data set. Objects in these sparse areas - that are required to separate clusters - are usually considered to be noise and border points.
The most popular[9] density based clustering method is DBSCAN.[10] In contrast to many newer methods, it features a well-defined cluster model called "density-reachability". Similar to linkage based clustering, it is based on connecting points within certain distance thresholds. However, it only connects points that satisfy a density criterion, in the original variant defined as a minimum number of other objects within this radius. A cluster consists of all density-connected objects (which can form a cluster of an arbitrary shape, in contrast to many other methods) plus all objects that are within these objects' range. Another interesting property of DBSCAN is that its complexity is fairly low - it requires a linear number of range queries on the database - and that it will discover essentially the same results (it is deterministic for core and noise points, but not for border points) in each run, therefore there is no need to run it multiple times. OPTICS[11] is a generalization of DBSCAN that removes the need to choose an appropriate value for the range parameter , and produces a hierarchical result related to that of linkage clustering. DeLi-Clu,[12] Density-Link-Clustering combines ideas from single-linkage clustering and OPTICS, eliminating the parameter entirely and offering performance improvements over OPTICS by using an R-tree index.
The key drawback of DBSCAN and OPTICS is that they expect some kind of density drop to detect cluster borders. Moreover, they cannot detect intrinsic cluster structures which are prevalent in the majority of real life data. A variation of DBSCAN, EnDBSCAN,[13] efficiently detects such kinds of structures. On data sets with, for example, overlapping Gaussian distributions - a common use case in artificial data - the cluster borders produced by these algorithms will often look arbitrary, because the cluster density decreases continuously. On a data set consisting of mixtures of Gaussians, these algorithms are nearly always outperformed by methods such as EM clustering that are able to precisely model this kind of data.
Density-based clustering with DBSCAN.
DBSCAN assumes clusters of similar density, and may have problems separating nearby clusters
OPTICS is a DBSCAN variant that handles different densities much better
Recent developments[edit]
In recent years considerable effort has been put into improving algorithm performance of the existing algorithms.[14][15] Among them are CLARANS (Ng and Han, 1994),[16] and BIRCH (Zhang et al., 1996).[17] With the recent need to process larger and larger data sets (also known as big data), the willingness to trade semantic meaning of the generated clusters for performance has been increasing. This led to the development of pre-clustering methods such as canopy clustering, which can process huge data sets efficiently, but the resulting "clusters" are merely a rough pre-partitioning of the data set to then analyze the partitions with existing slower methods such as k-means clustering. Various other approaches to clustering have been tried such as seed based clustering.[18]
For high-dimensional data, many of the existing methods fail due to the curse of dimensionality, which renders particular distance functions problematic in high-dimensional spaces. This led to new clustering algorithms for high-dimensional data that focus on subspace clustering (where only some attributes are used, and cluster models include the relevant attributes for the cluster) and correlation clustering that also looks for arbitrary rotated ("correlated") subspace clusters that can be modeled by giving a correlation of their attributes. Examples for such clustering algorithms are CLIQUE[19] and SUBCLU.[20]
Ideas from density-based clustering methods (in particular the DBSCAN/OPTICS family of algorithms) have been adopted to subspace clustering (HiSC,[21] hierarchical subspace clustering and DiSH[22]) and correlation clustering (HiCO,[23] hierarchical correlation clustering, 4C[24] using "correlation connectivity" and ERiC[25] exploring hierarchical density-based correlation clusters).
Several different clustering systems based on mutual information have been proposed. One is Marina Meilă's variation of information metric;[26] another provides hierarchical clustering.[27] Using genetic algorithms, a wide range of different fit-functions can be optimized, including mutual information.[28] Also message passing algorithms, a recent development in Computer Science and Statistical Physics, has led to the creation of new types of clustering algorithms.[29]
Evaluation of clustering results[edit]
Evaluation of clustering results sometimes is referred to as cluster validation.
There have been several suggestions for a measure of similarity between two clusterings. Such a measure can be used to compare how well different data clustering algorithms perform on a set of data. These measures are usually tied to the type of criterion being considered in assessing the quality of a clustering method.
Internal evaluation[edit]
When a clustering result is evaluated based on the data that was clustered itself, this is called internal evaluation. These methods usually assign the best score to the algorithm that produces clusters with high similarity within a cluster and low similarity between clusters. One drawback of using internal criteria in cluster evaluation is that high scores on an internal measure do not necessarily result in effective information retrieval applications.[30] Additionally, this evaluation is biased towards algorithms that use the same cluster model. For example k-Means clustering naturally optimizes object distances, and a distance-based internal criterion will likely overrate the resulting clustering.
Therefore, the internal evaluation measures are best suited to get some insight into situations where one algorithm performs better than another, but this shall not imply that one algorithm produces more valid results than another.[4] Validity as measured by such an index depends on the claim that this kind of structure exists in the data set. An algorithm designed for some kind of models has no chance if the data set contains a radically different set of models, or if the evaluation measures a radically different criterion.[4] For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex clusters neither the use of k-means, nor of an evaluation criterion that assumes convexity, is sound.
The following methods can be used to assess the quality of clustering algorithms based on internal criterion:
- The Davies–Bouldin index can be calculated by the following formula:
- where n is the number of clusters, is the centroid of cluster , is the average distance of all elements in cluster to centroid , and is the distance between centroids and . Since algorithms that produce clusters with low intra-cluster distances (high intra-cluster similarity) and high inter-cluster distances (low inter-cluster similarity) will have a low Davies–Bouldin index, the clustering algorithm that produces a collection of clusters with the smallest Davies–Bouldin index is considered the best algorithm based on this criterion.
- Dunn index (J. C. Dunn 1974)
- The Dunn index aims to identify dense and well-separated clusters. It is defined as the ratio between the minimal inter-cluster distance to maximal intra-cluster distance. For each cluster partition, the Dunn index can be calculated by the following formula:[31]
- where represents the distance between clusters and , and measures the intra-cluster distance of cluster . The inter-cluster distance between two clusters may be any number of distance measures, such as the distance between the centroids of the clusters. Similarly, the intra-cluster distance may be measured in a variety ways, such as the maximal distance between any pair of elements in cluster . Since internal criterion seek clusters with high intra-cluster similarity and low inter-cluster similarity, algorithms that produce clusters with high Dunn index are more desirable.
External evaluation[edit]
In external evaluation, clustering results are evaluated based on data that was not used for clustering, such as known class labels and external benchmarks. Such benchmarks consist of a set of pre-classified items, and these sets are often created by human (experts). Thus, the benchmark sets can be thought of as a gold standard for evaluation. These types of evaluation methods measure how close the clustering is to the predetermined benchmark classes. However, it has recently been discussed whether this is adequate for real data, or only on synthetic data sets with a factual ground truth, since classes can contain internal structure, the attributes present may not allow separation of clusters or the classes may contain anomalies.[32] Additionally, from a knowledge discovery point of view, the reproduction of known knowledge may not necessarily be the intended result.[32]
Some of the measures of quality of a cluster algorithm using external criterion include:
- Rand measure (William M. Rand 1971)[33]
- The Rand index computes how similar the clusters (returned by the clustering algorithm) are to the benchmark classifications. One can also view the Rand index as a measure of the percentage of correct decisions made by the algorithm. It can be computed using the following formula:
- where is the number of true positives, is the number of true negatives, is the number of false positives, and is the number of false negatives. One issue with the Rand index is that false positives and false negatives are equally weighted. This may be an undesirable characteristic for some clustering applications. The F-measure addresses this concern.
- The F-measure can be used to balance the contribution of false negatives by weighting recall through a parameter . Let precision and recall be defined as follows:
- where is the precision rate and is the recall rate. We can calculate the F-measure by using the following formula:[30]
- Notice that when , . In other words, recall has no impact on the F-measure when , and increasing allocates an increasing amount of weight to recall in the final F-measure.
- Pair-counting F-Measure is the F-Measure applied to the set of object pairs, where objects are paired with each other when they are part of the same cluster. This measure is able to compare clusterings with different numbers of clusters.
- Jaccard index
- The Jaccard index is used to quantify the similarity between two datasets. The Jaccard index takes on a value between 0 and 1. An index of 1 means that the two dataset are identical, and an index of 0 indicates that the datasets have no common elements. The Jaccard index is defined by the following formula:
- This is simply the number of unique elements common to both sets divided by the total number of unique elements in both sets.
- Fowlkes–Mallows index (E. B. Fowlkes & C. L. Mallows 1983)[34]
- The Fowlkes-Mallows index computes the similarity between the clusters returned by the clustering algorithm and the benchmark classifications. The higher the value of the Fowlkes-Mallows index the more similar the clusters and the benchmark classifications are. It can be computed using the following formula:
- where is the number of true positives, is the number of false positives, and is the number of false negatives. The index is the geometric mean of the precision and recall and , while the F-measure is their harmonic mean.[35] Moreover, precision and recall are also known as Wallace's indices and .[36]
- A confusion matrix can be used to quickly visualize the results of a classification (or clustering) algorithm. It shows how different a cluster is from the gold standard cluster.
- The Mutual Information is an information theoretic measure of how much information is shared between a clustering and a ground-truth classification that can detect a non-linear similarity between two clusterings. Adjusted mutual information is the corrected-for-chance variant of this that has a reduced bias for varying cluster numbers.
Clustering axioms[edit]
Given that there is a myriad of clustering algorithms and objectives, it is helpful to reason about clustering independently of any particular algorithm, objective function, or generative data model. This can be achieved by defining a clustering function as one that satisfies a set of properties. This is often termed as an Axiomatic System. Functions that satisfy the basic axioms are called clustering functions.[37]
Formal preliminaries[edit]
A partitioning function acts on a set of points along with an integer , and pairwise distances among the points in . The points are not assumed to belong to any specific larger set or space; the pairwise distances are the only data the partitioning function has about them.[citation needed] We may label the points in using the numbers . The pairwise distances define a distance function which should have the properties of a semimetric: for any , we must have , and if and only if . In other words, the distances must be nonnegative, symmetric, and two points have distance zero if and only if they are the same point.
A partitioning function takes a distance function on and an integer and returns a -partition of , a collection of non-empty disjoint subsets of whose union is . The sets making up the k-partition are the clusters. Two clustering functions are equivalent if and only if they output the same partitioning on all values of and .
Now in an effort to distinguish clustering functions from partitioning functions, we lay down some properties that one may like a clustering function to satisfy. Here is the first one. If is a distance function, then we define to be the same function with all distances multiplied by .
- Scale-invariance.
For any distance function , number of clusters , and scalar , we have
This property simply requires the function to be immune to stretching or shrinking the data points linearly. It effectively disallows clustering functions to be sensitive to changes in units of measurement - which is desirable. We would like clustering functions to not have any predefined hard-coded distance values in their decision process.
The next property ensures that the clustering function is "rich" in types of partitioning it could output. For a fixed and , Let Range() be the set of all possible outputs while varying .
- -richess.
For any number of clusters , Range() is equal to the set of all -partitions of
In other words, if we are given a set of points such that all we know about the points are pairwise distances, then for any partitioning , there should exist a such that . By varying distances amongst points, we should be able to obtain all possible -partitionings.
The next property is more subtle. We call a partitioning function "consistent" if it satisfies the following: when we shrink distances between points in the same cluster and expand distances between points in different clusters, we get the same result. Formally, we say that is a -transformation of if (a) for all belonging to the same cluster of , we have ; and (b) for all belonging to different clusters of , we have . In other words, is a transformation of such that points inside the same cluster are brought closer together and points not inside the same cluster are moved further away from one another.
- Consistency.
Fix . Let be a distance function, and be a -transformation of . Then
In other words, suppose that we run the partitioning function on to get back a particular partitioning . Now, with respect to , if we shrink in-cluster distances or expand between-cluster distances and run again, we should still get back the same result - namely .
The partitioning function is forced to return a fixed number of clusters: . If this were not the case, then the above three properties could never be satisfied by any function.[38] In many popular clustering algorithms such as -means, Single-Linkage, and spectral clustering, the number of clusters to be returned is determined beforehand – by the human user or other methods – and passed into the clustering function as a parameter.
- Biology, computational biology and bioinformatics
- Plant and animal ecology
- cluster analysis is used to describe and to make spatial and temporal comparisons of communities (assemblages) of organisms in heterogeneous environments; it is also used in plant systematics to generate artificial phylogenies or clusters of organisms (individuals) at the species, genus or higher level that share a number of attributes
- Transcriptomics
- clustering is used to build groups of genes with related expression patterns (also known as coexpressed genes). Often such groups contain functionally related proteins, such as enzymes for a specific pathway, or genes that are co-regulated. High throughput experiments using expressed sequence tags (ESTs) or DNA microarrays can be a powerful tool for genome annotation, a general aspect of genomics.
- Sequence analysis
- clustering is used to group homologous sequences into gene families. This is a very important concept in bioinformatics, and evolutionary biology in general. See evolution by gene duplication.
- High-throughput genotyping platforms
- clustering algorithms are used to automatically assign genotypes.
- Human genetic clustering
- The similarity of genetic data is used in clustering to infer population structures.
- Medical imaging
- On PET scans, cluster analysis can be used to differentiate between different types of tissue and blood in a three-dimensional image. In this application, actual position does not matter, but the voxel intensity is considered as a vector, with a dimension for each image that was taken over time. This technique allows, for example, accurate measurement of the rate a radioactive tracer is delivered to the area of interest, without a separate sampling of arterial blood, an intrusive technique that is most common today.
- IMRT segmentation
- Clustering can be used to divide a fluence map into distinct regions for conversion into deliverable fields in MLC-based Radiation Therapy.
- Business and marketing
- Market research
- Cluster analysis is widely used in market research when working with multivariate data from surveys and test panels. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers, and for use in market segmentation, Product positioning, New product development and Selecting test markets.
- Grouping of shopping items
- Clustering can be used to group all the shopping items available on the web into a set of unique products. For example, all the items on eBay can be grouped into unique products. (eBay doesn't have the concept of a SKU)
- Social network analysis
- In the study of social networks, clustering may be used to recognize communities within large groups of people.
- Search result grouping
- In the process of intelligent grouping of the files and websites, clustering may be used to create a more relevant set of search results compared to normal search engines like Google. There are currently a number of web based clustering tools such as Clusty.
- Slippy map optimization
- Flickr's map of photos and other map sites use clustering to reduce the number of markers on a map. This makes it both faster and reduces the amount of visual clutter.
- Software evolution
- Clustering is useful in software evolution as it helps to reduce legacy properties in code by reforming functionality that has become dispersed. It is a form of restructuring and hence is a way of directly preventative maintenance.
- Image segmentation
- Clustering can be used to divide a digital image into distinct regions for border detection or object recognition.
- Evolutionary algorithms
- Clustering may be used to identify different niches within the population of an evolutionary algorithm so that reproductive opportunity can be distributed more evenly amongst the evolving species or subspecies.
- Recommender systems
- Recommender systems are designed to recommend new items based on a user's tastes. They sometimes use clustering algorithms to predict a user's preferences based on the preferences of other users in the user's cluster.
- Markov chain Monte Carlo methods
- Clustering is often utilized to locate and characterize extrema in the target distribution.
- Social science
- Crime analysis
- Cluster analysis can be used to identify areas where there are greater incidences of particular types of crime. By identifying these distinct areas or "hot spots" where a similar crime has happened over a period of time, it is possible to manage law enforcement resources more effectively.
- Educational data mining
- Cluster analysis is for example used to identify groups of schools or students with similar properties.
- Typologies
- From poll data, projects such as those undertaken by the Pew Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.
- Others
- Field robotics
- Clustering algorithms are used for robotic situational awareness to track objects and detect outliers in sensor data.[39]
- Mathematical chemistry
- To find structural similarity, etc., for example, 3000 chemical compounds were clustered in the space of 90 topological indices.[40]
- Climatology
- To find weather regimes or preferred sea level pressure atmospheric patterns.[41]
- Petroleum geology
- Cluster analysis is used to reconstruct missing bottom hole core data or missing log curves in order to evaluate reservoir properties.
- Physical geography
- The clustering of chemical properties in different sample locations.
See also[edit]
|Wikimedia Commons has media related to Cluster analysis.
|This section may require cleanup to meet Wikipedia's quality standards. (October 2011)
See also[edit]
Related methods[edit]
- ^ Bailey, Ken (1994). "Numerical Taxonomy and Cluster Analysis". Typologies and Taxonomies. p. 34. ISBN 9780803952591.
- ^ Tryon, Robert C. (1939). Cluster Analysis: Correlation Profile and Orthometric (factor) Analysis for the Isolation of Unities in Mind and Personality. Edwards Brothers.
- ^ Cattell, R. B. (1943). The description of personality: Basic traits resolved into clusters. Journal of Abnormal and Social Psychology, 38, 476-506.
- ^ a b c d e f Estivill-Castro, Vladimir (20 June 200202). "Why so many clustering algorithms — A Position Paper". ACM SIGKDD Explorations Newsletter 4 (1): 65–75. doi:10.1145/568574.568575.
- ^ R. Sibson (1973). "SLINK: an optimally efficient algorithm for the single-link cluster method". The Computer Journal (British Computer Society) 16 (1): 30–34. doi:10.1093/comjnl/16.1.30.
- ^ D. Defays (1977). "An efficient algorithm for a complete link method". The Computer Journal (British Computer Society) 20 (4): 364–366. doi:10.1093/comjnl/20.4.364.
- ^ Lloyd, S. (1982). "Least squares quantization in PCM". IEEE Transactions on Information Theory 28 (2): 129–137. doi:10.1109/TIT.1982.1056489.
- ^ Hans-Peter Kriegel, Peer Kröger, Jörg Sander, Arthur Zimek (2011). "Density-based Clustering". WIREs Data Mining and Knowledge Discovery 1 (3): 231–240. doi:10.1002/widm.30.
- ^ Microsoft academic search: most cited data mining articles: DBSCAN is on rank 24, when accessed on: 4/18/2010
- ^ Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu (1996). "A density-based algorithm for discovering clusters in large spatial databases with noise". In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. pp. 226–231. ISBN 1-57735-004-9.
- ^ Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander (1999). "OPTICS: Ordering Points To Identify the Clustering Structure". ACM SIGMOD international conference on Management of data. ACM Press. pp. 49–60.
- ^ Achtert, E.; Böhm, C.; Kröger, P. (2006). "DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking". LNCS: Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science 3918: 119–128. doi:10.1007/11731139_16. ISBN 978-3-540-33206-0.
- ^ S Roy, D K Bhattacharyya (2005). "An Approach to find Embedded Clusters Using Density Based Techniques". LNCS Vol.3816. Springer Verlag. pp. 523–535.
- ^ D. Sculley (2010). "Web-scale k-means clustering". Proc. 19th WWW.
- ^ Z. Huang. "Extensions to the k-means algorithm for clustering large data sets with categorical values". Data Mining and Knowledge Discovery, 2:283–304, 1998.
- ^ R. Ng and J. Han. "Efficient and effective clustering method for spatial data mining". In: Proceedings of the 20th VLDB Conference, pages 144-155, Santiago, Chile, 1994.
- ^ Tian Zhang, Raghu Ramakrishnan, Miron Livny. "An Efficient Data Clustering Method for Very Large Databases." In: Proc. Int'l Conf. on Management of Data, ACM SIGMOD, pp. 103–114.
- ^ Can, F.; Ozkarahan, E. A. (1990). "Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases". ACM Transactions on Database Systems 15 (4): 483. doi:10.1145/99935.99938.
- ^ Agrawal, R.; Gehrke, J.; Gunopulos, D.; Raghavan, P. (2005). "Automatic Subspace Clustering of High Dimensional Data". Data Mining and Knowledge Discovery 11: 5. doi:10.1007/s10618-005-1396-1.
- ^ Karin Kailing, Hans-Peter Kriegel and Peer Kröger. Density-Connected Subspace Clustering for High-Dimensional Data. In: Proc. SIAM Int. Conf. on Data Mining (SDM'04), pp. 246-257, 2004.
- ^ Achtert, E.; Böhm, C.; Kriegel, H. P.; Kröger, P.; Müller-Gorman, I.; Zimek, A. (2006). "Finding Hierarchies of Subspace Clusters". LNCS: Knowledge Discovery in Databases: PKDD 2006. Lecture Notes in Computer Science 4213: 446–453. doi:10.1007/11871637_42. ISBN 978-3-540-45374-1.
- ^ Achtert, E.; Böhm, C.; Kriegel, H. P.; Kröger, P.; Müller-Gorman, I.; Zimek, A. (2007). "Detection and Visualization of Subspace Cluster Hierarchies". LNCS: Advances in Databases: Concepts, Systems and Applications. Lecture Notes in Computer Science 4443: 152–163. doi:10.1007/978-3-540-71703-4_15. ISBN 978-3-540-71702-7.
- ^ Achtert, E.; Böhm, C.; Kröger, P.; Zimek, A. (2006). "Mining Hierarchies of Correlation Clusters". Proc. 18th International Conference on Scientific and Statistical Database Management (SSDBM): 119–128. doi:10.1109/SSDBM.2006.35. ISBN 0-7695-2590-3.
- ^ Böhm, C.; Kailing, K.; Kröger, P.; Zimek, A. (2004). "Computing Clusters of Correlation Connected objects". Proceedings of the 2004 ACM SIGMOD international conference on Management of data - SIGMOD '04. p. 455. doi:10.1145/1007568.1007620. ISBN 1581138598.
- ^ Achtert, E.; Bohm, C.; Kriegel, H. P.; Kröger, P.; Zimek, A. (2007). "On Exploring Complex Relationships of Correlation Clusters". 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007). p. 7. doi:10.1109/SSDBM.2007.21. ISBN 0-7695-2868-6.
- ^ Meilă, Marina (2003). "Comparing Clusterings by the Variation of Information". Learning Theory and Kernel Machines. Lecture Notes in Computer Science 2777: 173–187. doi:10.1007/978-3-540-45167-9_14. ISBN 978-3-540-40720-1.
- ^ Alexander Kraskov, Harald Stögbauer, Ralph G. Andrzejak, and Peter Grassberger, "Hierarchical Clustering Based on Mutual Information", (2003) ArXiv q-bio/0311039
- ^ Auffarth, B. (2010). Clustering by a Genetic Algorithm with Biased Mutation Operator. WCCI CEC. IEEE, July 18–23, 2010. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.869
- ^ B.J. Frey and D. Dueck (2007). "Clustering by Passing Messages Between Data Points". Science 315 (5814): 972–976. doi:10.1126/science.1136800. PMID 17218491 Papercore summary Frey2007
- ^ a b Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press. ISBN 978-0-521-86571-5.
- ^ Dunn, J. (1974). "Well separated clusters and optimal fuzzy partitions". Journal of Cybernetics 4: 95–104. doi:10.1080/01969727408546059.
- ^ a b Ines Färber, Stephan Günnemann, Hans-Peter Kriegel, Peer Kröger, Emmanuel Müller, Erich Schubert, Thomas Seidl, Arthur Zimek (2010). "On Using Class-Labels in Evaluation of Clusterings". In Xiaoli Z. Fern, Ian Davidson, Jennifer Dy. MultiClust: Discovering, Summarizing, and Using Multiple Clusterings. ACM SIGKDD.
- ^ W. M. Rand (1971). "Objective criteria for the evaluation of clustering methods". Journal of the American Statistical Association (American Statistical Association) 66 (336): 846–850. doi:10.2307/2284239. JSTOR 2284239.
- ^ E. B. Fowlkes & C. L. Mallows (1983), "A Method for Comparing Two Hierarchical Clusterings", Journal of the American Statistical Association 78, 553–569.
- ^ L. Hubert et P. Arabie. Comparing partitions. J. of Classification, 2(1), 1985.
- ^ D. L. Wallace. Comment. Journal of the American Statistical Association, 78 :569– 579, 1983.
- ^ R. B. Zadeh, S Ben-David. "A Uniqueness Theorem for Clustering", in Proceedings of the Conference of Uncertainty in Artificial Intelligence, 2009.
- ^ J Kleinberg, "An Impossibility Theorem for Clustering", Proceedings of The Neural Information Processing Systems Conference 2002
- ^ Bewley A. et al. "Real-time volume estimation of a dragline payload". "IEEE International Conference on Robotics and Automation",2011: 1571-1576.
- ^ Basak S.C., Magnuson V.R., Niemi C.J., Regal R.R. "Determining Structural Similarity of Chemicals Using Graph Theoretic Indices". Discr. Appl. Math., 19, 1988: 17-44.
- ^ Huth R. et al. "Classifications of Atmospheric Circulation Patterns: Recent Advances and Applications". Ann. N.Y. Acad. Sci., 1146, 2008: 105-152
External links[edit]
- Machine Learning at DMOZ
- Data Mining at DMOZ


text:
Commonly Asked QuestionsView Our Full FAQs
Invite Your friends - trip organization tool
Use our online tool to invite friends on an upcoming adventure!Start Your Search
Massachusetts White Water Rafting
|Reserve this trip
View Our Exciting Media Gallery
Enjoy Whitewater Rafting in Massachusetts!
In the northern Berkshire Mountains of Western Massachusetts,
Crab Apple Whitewater is pleased to bring you five different white water rafting adventures. River rafting trips range from mild to WILD, full or half-day in rafts and inflatable kayaks. From beginners to more experienced rafters, our family will enjoy taking your family, group or organization down these beautiful rivers. Our beautiful base camp is located on the banks of the Deerfield River along the Mohawk Trail in Charlemont. Come see us this season!
Deerfield River - Fife Brook Section
Class I-III whitewater, Minimum age 8,
Deerfield River - Monroe Bridge Section
Class III-IV whitewater, Minimum age 15,
Deerfield River Funyaks
Class I-II whitewater, Minimum ages 5 for double,
Deerfield River Half-Day Family Raft Trip
Easy Class I-II whitewater, Minimum age 5,
Miller's River
Class II-IV whitewater, Minimum age 12,

subset: commoncrawl


text:
SUU Digital Library
In addition to this general society the students of the Normal have a "Debating Club," and a "Literary and Domestic Art Society." The former is conducted exclusively by men, the latter by women.
The Athletic Association is active not only in promoting games and out of door sports, but it is also taking an interest in other phases of physical education.
The student body of the Branch Normal is self-governing. An organization known as the "Branch' Normal School City" has been effected, its functions being similar to those of cities of the first class. Through this government the students make their own laws and enforce them. By thus affording a very practical training in civics, the system does much in preparing the students for the greater duties imposed by citizenship.
To further the musical interests of the school, especially in the way of chorus and glee singing, a society known as "The Choral" has been organized. All students of the Normal are eligible to membership.
In addition to "The Choral," the Ladies' Chorus, the Gentlemen's Quartet Club, and the Mandolin and Guitar Club have become permanent features of our music department. Students possessing the desired qualifications may apply to the instructor in music for membership in these clubs.
The President and Faculty of the University will co-operate with the instructors of the Branch Normal School in giving a course of public lectures during the winter months.
These lectures will be on various scientific, literary, and philosophical subjects of popular interest, the aim being to give the citizens generally some of the educational advantages of the Normal School. Lectures illustrated with stereopticon views will be given frequently.
Students who wish to study music in addition to that offered in


text:
Wedding Florists near pittsfield,nh
Other Wedding Vendors near pittsfield,nh:
|Wedding Band (28)
|Wedding Beauty & Health (112)
|Wedding Catering (72)
|Wedding Ceremony Music (18)
|Wedding DJ (134)
|Wedding Dress & Attire (72)
|Wedding Event Rentals (41)
|Wedding Favors & Gifts (37)
|Wedding Flowers (114)
|Wedding Invitations (57)
|Wedding Jewelry (60)
|Wedding Lighting & Decor (13)
|Wedding Officiant (74)
|Wedding Photography (347)
|Wedding Planning (78)
|Wedding Transportation (19)
|Wedding Travel Agents (24)
|Wedding Venue (191)
|Wedding Videography (40)
|Wedding Cakes (95)
You've chosen the maximum (5) items in this category. Compare your items or remove and replace with another.Compare Now
0 Vendors Selected. Select up to 5 vendors to compare in one of the following categories: Venues, Bands, Catering, Photography or Videographers.
Lotus Floral Designs LLC43 Reviews
Wedding & Event Flowers
Lotus is couture. A new trend is set with every wedding and every couple we are privileged to work with. Lotus style is all encompassing; it is...(more)
Britton Designs2 Reviews
Sandown, NH | 603-247-7794
Having a passion for all things beautiful, especially flowers. Britton Designs offers brilliantly elegant floral arrangements for your special day....(more)
Contact Us Website
Book you 2014 wedding flowers with Britton Designs now and receive complimentary flower delivery on your wedding day! Offer available for all weddings within a 100 mile radius of Sandown,NH 03873 Call today! 603-247-7794
Anne's Florals & Gifts30 Reviews
Hudson, NH | 603-889-9903
Anne's reputation for quality, attention to detail, and design flair is recognized throughout the floral industry. For 30 years, she has created...(more)
Emily Herzig Floral Studio55 Reviews
Littleton, NH | 603-444-7600
Emily Herzig Floral Studio has earned her reputation for natural, beautiful, sophistication for wedding and event design through out New...(more)
Naturally Elegant Designs20 Reviews
Flowers and Wedding Planning
Keene, NH | 603-358-9800
We serve New Hampshire, Vermont, and Massachusetts - We have been designing weddings for years, from the coast to the mountains. Whatever your...(more)
Contact Us Website
When you contract with me for your wedding flowers and tell me you found me on Wedding Wire, I'll provide your Maid of Honor bouquet for free.
Ford Flower Co. LLC40 Reviews
Salem, NH | 603-893-9955
New Hampshire's leading florist Our shop can reached by phone at (603)893-9955 or at (800)448-1061, or by fax at (603)890-6228. We are open Monday...(more)
Harrington Flowers LLC50 Reviews
We cover most of New England.
Londonderry, NH | 603-548-3603
My goals are simple...meet, exceed and work towards your personal style, taste and budget with chic, stylish and extraordinary work and personal...(more)
Contact Us Website
Book your wedding with us in March and recieve one free bouquet for the Maid of Honor! Must book bouquets, ceremony flowers, and reception flowers. Can not be combined with any other offers or coupons. no cash valueAlso recieve a free toss bouquet Wedding Wire only
Wisteria Flower Shoppe17 Reviews
We design what you have in mind!
Derry, NH | 603-434-4600
Wisteria Flower Shoppe located in the vintage business district of Derry, NH, opened in 2006 with the idea of creating a place for flower...(more)
Sorella Flower Company2 Reviews
Contoocook, NH | 603-746-6889
Private User's review: Sorella Flower Company was very easy to work with and very dependable. They did the flowers for my wedding. Their... (more)
Art of Nature2 Reviews
New London, NH | 603-526-2638
Known for her creative and innovative style, Mauli's extensive background in both floral design and art translates into a dynamic and unparalleled...(more)
Drinkwater Flowers & Design9 Reviews
Honoring the beauty of each individual flower/bride in all of New England
Hampton , NH | 603-926-4181
From Boston MA to Kennebunkport ME Drinkwater Flowers has a refreshing organic approach to wedding flowers, composing unusual flowers in lush...(more)
Flower Kiosk31 Reviews
Beauty You Will Remember
Portsmouth, NH | 603-436-1234
From the most intimate garden tea party to the grandest country club gala, we will create exquisite floral arrangements to fit the season, your...(more)
Paisley Floral Design Studio30 Reviews
Manchester, NH | 603-493-8386
It's pretty simple...but my passion for color, texture, and design began as a young girl, and has transitioned into years of experience in floral...(more)
Tiger Lilies Floral Boutique7 Reviews
Epping, NH | 603-734-4565
Tiger Lilies Floral Boutique specializes in creating beautiful custom bridal bouquets, centerpieces and bridal party pieces for every...(more)
Lyndsey Loring Design72 Reviews
Serving NH, ME & MA | 603-743-4855
Just launched our all NEW website!! Offering fabulous custom wedding flower packages to fit any style & budget. We specialize in the elegant...(more)
Simple Bouquets40 Reviews
Serving NH, MA, ME and VT
Tilton, NH | 603-286-7200
For the discerning bride.... There are things that just cannot be compromised. Where only the finest will suffice. In a Simple...(more)
Lady Slipper Creations - Innovative Floral Design LLC13 Reviews
"Enriching Lives and Living Spaces with Color and Style"
Chester, NH | 603-244-8856
We are a full service event/corporate and personal floral design studio located in Southern New Hampshire. We offer complimentary consultations...(more)
Contact Us Website
When you book your 2014 wedding with us and reserve personal, ceremony and reception flowers - We'll "throw" in a complimentary toss bouquet! Dates are booking fast so call us today for your consultation! Remember weekend and evening appointments are available!
La Flora Couture9 Reviews
Wedding Floral Designs & Event Stylist.
Nashua, NH | 603-703-7402
Specializing in Weddings & Events. La Flora Couture fuses flowers with fashion to create a fun, personalized and unique floral experience. ...(more)
The Country Daisy Florist9 Reviews
Let us help make your dream a reality
Farmington, NH | 603-755-3715
We are a full service florist here for all your floral wedding needs. With over 17 years experience, we are here to help you turn your Wedding...(more)


text:
Published: April 22, 2014 at 05:54 p.m.
Daniel Jeremiah and Brian Baldinger discuss who the New Orleans Saints could target in the draft, including a couple of players who thrived on the West Coast.
Published: April 21, 2014 at 03:27 p.m.
After a controversial, heart-wrenching loss in the Superdome last year, Jim Harbaugh and his San Francisco 49ers are looking for revenge against the New Orleans Saints in 2014.
Published: April 17, 2014 at 09:12 p.m.
Dave Dameshek unveils the six NFC teams that he expects in the postseason and explains why one team will have the one seed, but another will go to the Super Bowl.
Published: April 14, 2014 at 08:33 p.m.
The "NFL Total Access" crew breaks down the Philadelphia Eagles' offseason movement -- and one of their new additions, safety Malcolm Jenkins, elucidates how the team's play will morph in 2014.
Published: April 10, 2014 at 09:11 p.m.
The Tampa Bay Buccaneers made some big moves during the offseason, including hiring a new head coach, but will they be able to knock off the New Orleans Saints, Carolina Panthers and Atlanta Falcons?
Published: April 10, 2014 at 08:29 p.m.
Albert Breer discusses the offseason moves made by the New Orleans Saints and the team's strategy for getting younger and faster.
Published: April 10, 2014 at 09:42 a.m.
The Tampa Bay Buccaneers may have undergone a visual overhaul this offseason -- but did they make the necessary changes on the field to be a contender in the NFC South?
Published: April 4, 2014 at 05:17 p.m.
Will Selva has the latest on Champ Bailey's new contract with the New Orleans Saints, the release of Chris Johnson and Pete Carroll's contract extension with the Seattle Seahawks.
Published: April 3, 2014 at 03:15 p.m.
Could Matty Ice be Matty Sunshine? See how Atlanta Falcons quarterback Matt Ryan could have ended up near the ocean if the 2008 draft had a makeover.
Published: April 2, 2014 at 09:49 a.m.
Mark Kriegel, Shawne Merriman and Terrell Davis tell you what the New Orleans Saints need to do in 2014 to make a run in the playoffs.
Published: March 26, 2014 at 09:42 a.m.
New Orleans Saints head coach Sean Payton talks about when the team could start looking for a younger quarterback to learn under veteran Drew Brees.
Published: March 20, 2014 at 09:14 p.m.
New Orleans Saints QB Drew Brees gives his thoughts on NFL.com's Best QB of All Time Bracketology. Go to NFL.com/bracketology to vote today.
Published: March 20, 2014 at 08:56 p.m.
New Orleans Saints quarterback Drew Brees discusses what tight end Jimmy Graham brings to the offense and weighs in on the loss of running back Darren Sproles.
Published: March 19, 2014 at 10:06 a.m.
New Orleans Saints defensive end Cameron Jordan goes through the team's additions and losses this offseason and also explains how the defense turned it around in 2013 after a poor 2012 season.
Published: March 14, 2014 at 08:42 p.m.
Safety Jairus Byrd stops by "NFL Total Access" to discuss what attracted him to the New Orleans Saints organization and playing for defensive coordinator Rob Ryan.
Published: March 12, 2014 at 03:42 p.m.
NFL Draft Analyst Daniel Jeremiah says there is a lot of value to be found in the 2014 NFL draft at the running back position for the New Orleans Saints and Florida State RB Davonta Freeman might be that guy.
Published: March 12, 2014 at 03:24 p.m.
Watch BYU DL Eathyn Manumaleuna work out at the 2014 NFL Scouting Combine.
Published: March 12, 2014 at 01:20 p.m.
Ian Rapoport reports there are several teams interested in New Orleans Saints running back Darren Sproles, but the running back would rather be released than traded.
Published: March 11, 2014 at 10:23 p.m.
With safety Jairus Byrd signing a 6-year deal with the New Orleans Saints, how much of an impact will the play-making defender make for the Saints? The "NFL Total Access" crew breaks it down.
Published: March 11, 2014 at 08:05 p.m.
Are the Saints making a big mistake by shopping running back Darren Sproles? Marshall Faulk and Steve Mariucci weigh in on New Orleans' decision on "NFL Total Access."
Published: March 7, 2014 at 09:11 p.m.
Will Selva has the latest on New Orleans Saints wide receiver Lance Moore's release, the details of Jacksonville Jaguars quarterback Chad Henne's 2-year contract and more from around the NFL.
Published: March 7, 2014 at 09:10 p.m.
With running back Darren Sproles most likely gone from the New Orleans Saints, how will one of the top offenses in the NFL recover? The "NFL Total Access" crew weighs in.
Published: March 7, 2014 at 09:00 p.m.
NFL Media Insider Ian Rapoport joins "NFL Total Access" with the latest on how the release of running back Darren Sproles affects the New Orleans Saints salary cap this offseason.
Published: March 7, 2014 at 07:04 p.m.
One of the league's top free agents, safety Jairus Byrd added four interceptions during the 2013 season bringing his career total to 22.
Published: March 4, 2014 at 03:58 p.m.
New Orleans Saints defensive coordinator Rob Ryan braved the elements Tuesday to serve as Grand Marshal of the Krewe of Argus Mardi Gras parade in New Orleans.
© 2014 NFL Enterprises LLC. NFL and the NFL shield design are registered trademarks of the National Football League.The team names, logos and uniform designs are registered trademarks of the teams indicated. All other NFL-related trademarks are trademarks of the National Football League. NFL footage © NFL Productions LLC. PRIVACY POLICY | Terms & Conditions


text:
- Quick Links
- Academics (Credit)
- Continuing Education (Non-Credit)
- ----------------------
- HCC Portal
- Moodle
- WebAdvisor
- Student Email
- College Central Network
- Emergency Alert Sign-Up
- Transcript Requests
- ----------------------
- Alumni Association
- HCC Foundation
- Volunteers
- ----------------------
- Administrator on Duty
- Directions and Maps
- News and Events
- Athletics
- Library
- Jobs
USAF Brass Ensemble performs June 7
Submitted by Leigh-Anne Mauk on Thu, 05/31/2012 - 1:45pm
The Hagerstown Community College Alumni Association will host the U.S. Air Force Heritage Brass Ensemble as part of its annual Red, White and Blue Concert Series, on Thurs., June 7, at 6:30 p.m., in HCC's Alumni Amphitheater.
The U.S. Air Force Heritage Brass Ensemble is a five-member quintet that first began performing in 1973. Well-known for its showmanship, the band performs a variety of jazz, popular and patriotic music under the direction of Technical Sergeant Jason Foster.
Co-sponsored by the Hagerstown-Washington County Convention and Visitors Bureau, this concert is free and is open to the public. Concessions will be available for purchase. Please note that no outside food is permitted on the amphitheater grounds.
For more information about the Red, White and Blue Summer Concert Series, call 240-500-2346, or go to www.hagerstowncc.edu/alumni-friends. To learn more about the U.S. Air Force Heritage Brass Ensemble, go to www.heritageofamericaband.af.mil/ensembles.


text:
8205 University Ave NE, Spring Lake Park, MN | Directions 5543245.117070 -93.263803
Editorial Review by Citysearch Editors – Christian-focused books, Bibles and teaching aids. Also sells church supplies, music, videos and software. Spanish language materials available.
Sign in with Facebook Sign in with Facebook to see what your friends are up to!
Do you have a review of Northwestern Bookstores on your blog? If you include the Citysearch image link (shown below) in your post, we'll automatically link to your review from the business profile page. Here's how our Linkback system works:
The Linkback system is free and automatic. It's really that easy.
To link to our business profile page, copy the html below to your web page.


text:
But more often conflict creates free-fall, which will define the 2012 Pittsburgh Steelers if they can't beat the Cincinnati Bengals in a game today that will likely determine a wildcard playoff berth.
The Steelers have lost two in a row and four of their last five. And after throwing a game-deciding interception in overtime, quarterback Ben Roethlisberger criticized offensive coordinator Todd Haley's play-calling.
Roethlisberger walked into Haley's office on Monday and apologized, and at the time Haley didn't know why. That's how little the media matters to him.
"There are going to be ups and downs and frustrations," Haley said. "But it's never been an issue of being on the same page."
Are the Steelers still a team in conflict? Has the Roethlisberger-Haley dust-up faded? Or was it merely the projection of a hopeful media?
That's the question for the scuffling 7-7 Steelers today as they host a red-hot 8-6 Cincinnati Bengals team that's won five of its last six games.
"Well, your scuffles get over in a week and so can your hotness," said Bengals coach Marvin Lewis. "What happened last week doesn't matter a bit for this game. We know we're playing a very good team led by a great quarterback, and we've got to play well."
The Bengals had fallen to 3-4 after a 24-17 loss to the Steelers in Cincinnati. And after their ensuing bye week they fell to 3-5 with an 8-point loss to the Denver Broncos.
And then the Bengals got hot. It has coincided with the increased productivity of a running game that was ranked 25th in the NFL after Game 9 but is now ranked 11th.
"It helps to have A.J. Green out there on the edge," said Bengals offensive coordinator Jay Gruden. "Teams are doubling him with a safety, giving us one less guy to block."
That will certainly be the case for the Steelers' defense today. It comes into the game without its No. 1 cornerback, Ike Taylor, and a No. 2 cornerback in Keenan Lewis who left last week's game after aggravating a hip flexor injury and who missed most of this week's practices after aggravating the injury on Wednesday.
Lewis promised to play, but there's little doubt the Steelers will need a safety over the top to keep Green from beating them deep.
So the next point of emphasis for coordinator Dick LeBeau will be stopping BenJarvus Green-Ellis, who's averaged 108.6 rushing yards in the Bengals' last five games.
Green-Ellis has passed the 1,000-yard barrier for the second time in his career with 1,080. His feat of four 100-yard games in the last five hasn't been accomplished by a Bengal since Paul Robinson did it in the franchise's inaugural 1968 season.
Green-Ellis is a 220-pounder who leads the NFL with 14 third-and-1 conversions.
The Steelers held him to 69 yards on 18 carries in the previous meeting, and 9 yards on 5 carries last season when he shared duties with Kevin Faulk with the New England Patriots.
Conversely, the Steelers have gone cold along with their running game. They've averaged only 71 yards rushing the last four games, or since Willie Colon and Mike Adams were injured.
Colon is on injured reserve, replaced by first-round pick David DeCastro, and Adams is listed as doubtful this week, replaced by seventh-round draft pick Kelvin Beachum.
The rookies will line up on the right side against the strength of the Bengals' defense, linemen Geno Atkins and Carlos Dunlap.
"They are a matchup that is as good as any we've seen all year," said Haley. "We have to play our best game up front, and handle them in the run and pass."
Because the Steelers do not possess the manpower required to run the ball effectively, they'll likely try to outscore the Bengals with their passing game.
So, the big question remains: Can conflict be a good thing?
"Yeah," Haley said with a laugh. "You are talking to arguably one of the most confrontational, at least perception-wise.
"I think healthy conflict, discussion, banter, is good. It gets you to the right place. It gets everybody on the same page and where you need to be. Like I've said a number of times, when I make a call into Ben's ear in the game, I am putting complete faith in him to make it a good call or the best call it can be. The same for him, when he hears my voice, he has to trust and believe that the play coming in is giving him and us the best chance to succeed. You develop that through these Sunday battles and battles against other teams. That's where the trust and camaraderie develops. That's what has occurred.
"There are going to be ups and downs because there is emotion, and there is a lot at stake every single week for all of us. We've put a lot of time, effort and sacrifice into this. That's just the way it is. I think a little bit of healthy conflict is a good thing."
MSN Privacy
Advertise on MSN
About our ads
© 2014 Microsoft


text:
Search Results
252 Search Results found for:
January 01, 1990 - October 14, 2012 + LA Observed
The Magic of Magic Johnson
Twenty years after Magic Johnson announced that he had the AIDS virus, Kevin Roderick reflects on how the world looks at HIV, and what the...
Occupying LA
It's been 30 days now since Occupy LA's protesters set up camp on the lawn of City Hall...
LA Is Money
Today was my first day in a new club. Like a lot of other Angelenos, I've now been caught in an Obamajam....
Music in Historic Places
I don't listen to a lot of classical music, but some. I'm glad it's still available on the radio here. In many parts of the...
Nerds We Have Known
The death of Steve Jobs touched a lot of people, to understate an obvious fact.
Saving Their Dad
In the movie 127 Hours, James Franco survives in the wild by cutting off his own arm. Hollywood loves stories like that -- pulled from...
Obama Jamming
If you're listening in your car, there's a decent chance you're not moving. On this Monday the traffic jam you're in could be more intense...
Fear of the Unknown
No one needs the new poverty numbers to know there's a lot of anxiety in the land. But the image is pretty graphic: the highest...
East West Axis
Earlier today, men in suits and hardhats held shovels and pretended to dig in dirt for the cameras. These shovel-turning ceremonies used to occur all...
Wild LA
Unless you go to school, or have kids who do, Labor Day isn't the marker of time here that it is in less summery places....
Villaraigosa, the Legacy Years
Whenever you hear the media start talking about a politician's legacy, you know the end game has begun. Whether due to health, defeat or the...
Season of the Maps
You're excused for feeling like the presidential campaign – the 2012 campaign -- has already gone on too long. Every four years seems to come...
Stadium Politics
I was in Seattle last week and took the chance to do a little LA field research. Since the hottest local political issue here right...
Hollywood Sign as Icon
Los Angeles has only a few structures that have survived long enough for most to agree they've reached the status of icon...
From Mulholland to Bean
On the LA Observed website, this has been a summer for obituaries about talented people....


text:
UK breakdown coverGet a quote
– buy online
Arrange cover over the phone
Call us on 0800 085 2721
Broken down?
We can help – call us now
0800 88 77 66
There are 13 results - Show results on a map Print this page
Showing 1 - 10 of 13
Some 220 acres of beautiful countryside, which include 27 holes of golf and 12 lakes, combine to form a rather spectacular backdrop to this impressive hotel. Bedroom styles vary, from ultra smart, mod...More about Hellidon Lakes Golf & Spa Hotel
Located in the quiet village of Badby in the Northamptonshire countryside, a warm welcome awaits at this beautiful Georgian style family home. Spacious bedrooms and large comfortable beds will offer a...More about Bunkers Hill House
Dating back to the 15th century, this delightful hotel is peacefully located in beautiful gardens designed by 'Capability' Brown. Spacious, individually designed bedrooms and stylish public areas are ...More about Fawsley Hall
This modern, striking hotel overlooking Drayton Water boasts spacious public areas that include a good range of banqueting, meeting and leisure facilities. It is a popular venue for conferences. Bedro...More about Daventry Court Hotel
The Olde Coach House has been totally renovated. The public areas are in keeping with the building's vintage, but with all the modern amenities expected by today's guests. There is a large bar area an...More about Olde Coach House
High quality, budget accommodation ideal for both families and business travellers. Spacious, en suite bedrooms feature tea and coffee making facilities, and Freeview TV in most hotels. Internet acces...More about Premier Inn Daventry
The Narrow Boat at Weedon has a superb canal side location with easy access to motorways and is just one hour from London. There are seven comfortable en suite bedrooms at the rear of the property all...More about Narrow Boat at Weedon
This Northamptonshire farmhouse has been run by Brian and Carrie Hart for more than twelve years. They offer full English breakfast, along with luxurious en suite bedrooms and a guest lounge with log ...More about Murcott Mill Farmhouse B&B
At Innkeeper's Lodge you'll find accommodation with comfort and character in equal measure, and everything needed for a relaxing stay, from easy check-in and free parking to complimentary breakfast an...More about Innkeeper's Lodge Rugby, Dunchurch
Located in the picturesque Warwickshire countryside and within easy reach of motorway networks, this hotel offers modern, comfortable and well-equipped accommodation with a relaxed and friendly welcom...More about Draycote Hotel


text:
818 W 46th St, Minneapolis, MN | Directions 5541944.919674 -93.290543
Neighborhoods: East Harriet, Southwest
New full service wine bar! – New Menu, great new chef-
Java Jacks has re-emerged as Jacks food and drink- the new menu is American comfort food and will be well recieved in its S Mineapolis neighborhood.
Try the roast chicken, spaghetti and meatballs, charcuterie plate- all made in-house by a very creative and talented chef.
Great beer and wine lists too!
Decent coffee, rude staff – I've been living in S. Minneapolis for a couple years now but have only recently ventured out to this small coffee shop. I've been there a few times for their regular coffees, which are average. Then on one occasion, which was my last, I ordered a soy decaf drink. When the young man handed it to me I asked if it was decaf and his response was no, it's soy. When I asked him again if it was decaf, he annoyed response was 'you asked for soy'. Where do you get these people who don't know the difference between milk and coffee?? After being treated rudely by someone who had no idea what they were talking about I left and will not return.
Will not return. – A couple of weeks ago, my mom, sister and I finally made it over to Java Jack's after repeated recommendations from a family friend. Upon arrival, we were optimistic about the entertainment despite watery, lukewarm drinks. We were surprised about the $5 cover as we were under the impression the music was free on Friday nights. But no biggie, we paid the friendly man at the door and ventured further inside. Unfortunately, the performers were painfully horrible, to the point that I felt myself becoming embarrassed on their behalf. Their on-stage banter was worse, consisting of inside jokes to the audience (apparently all regulars from the neighborhood) mixed with awkward and boring information about their personal lives. The performers may have been even more obnoxious than the dozen or so poorly behaved little kids running around the place. Needing a break to decide if we wanted to stick it out or abandon ship, my group convened by the restroom. The Nice Man who took our cover charge saw us debating and encouraged us to come back downstairs for the headliner, whom we had come to see in the first place. Okay, okay. After all, we'd come this far. We returned to catch the end of a nice little number from the evening's headliner, only to be accosted by some rude and hyper man who emerged from a back room to lecture us about the place being over capacity and how the fire department was going to come and throw him in jail. What?? We had already paid, we told the man, so they should have factored us into the capacity equation when we showed up. He then launched into an interrogation, basically accusing us of lying about having paid the cover. He reluctantly allowed us to stay, but we finally called it a night when we turned around and saw Rude Guy whispering to Nice Money-Taking Guy while glaring and pointing at us. Wow. I?ve never been treated as rudely in my life, or felt as unwelcome. Java Jack's couldn't pay us to return.
A funky coffeehouse in south Minneapolis. – In Short
There's a den area filled with 1950s lounge furniture, plenty of so-so art for sale, a news rack with anything from The New York Times to Spin to Metropolitan Home. On most days, a long line of locals wait for their morning dose of French roast and cinnamon buns. Those who choose to sit awhile, perhaps lingering over a bowl of chili or lefse have a choice of smoking or no-smoking rooms.
Sign in with Facebook Sign in with Facebook to see what your friends are up to!
Do you have a review of Java Jack's Coffee Cafe Inc on your blog? If you include the Citysearch image link (shown below) in your post, we'll automatically link to your review from the business profile page. Here's how our Linkback system works:
The Linkback system is free and automatic. It's really that easy.
To link to our business profile page, copy the html below to your web page.


text:
For sale, a 1954 Allard K-3 all matching numbers with the original 331 Cubic Inch Cadillac V8 Engine and 3 Speed Manual Gearbox. One... More
ALLARD: 1952 K2 roadster, black with black interior, complete with sand colored soft top, side shields, and black tonneau cover, chrome 72-spoke wire wheels,... More
ALLARD: 1951 K2, Ford flathead, 3,000 miles, few dings, stored past ten years, runs fine, $40,000. 843-546-2455, SC; [email]... More


text:
Blairsville CBOC
1294 Highway 515 East, Suite 100, Blairsville, GA 30512Please go to MapQuest maps* for more information
* External Link Disclaimer:
By clicking on the link marked with asterisk (*), you will leave the Department of Veterans Affairs Website. VA does not endorse and is not responsible for the content of the linked website.
Veterans Crisis Line:
1-800-273-8255 (Press 1)
Social Media
U.S. Department of Veterans Affairs | 810 Vermont Avenue, NW Washington DC 20420
Last updated June 23, 2013.


text:
Main Content
Order today, receive no later than
Order today, receive no later than
Wednesday, April 30th
Some limitations apply
Haul your gear in style with this Home & Away drawstring gym bag featuring two sides of team colors with the school name on one side and logo on the other!
- Team logo and colors
- Screen print graphics
- Drawstring closure
- Water resistant
- Double-reinforced bottom corners
- Imported
- 100% Nylon construction
- Officially licensed collegiate product
Hawkeyes Customers also liked:
Special event items are produced by manufacturers only after the outcome of a game or event. These are advanced sale items and will ship immediately after they are received in our warehouse.
Manufacturer direct items are shipped directly from the manufacturer. These items are not available for international or expedited shipping.
Customized items can be personalized with options such as your name, your favorite number, and/or designs. Some options may be limited by league rules.


text:
Advancing freedom and prosperity by unleashing free enterprise, protecting...
Read more about Energy & Environment
Demand is increasing faster than supplies while much of the world's oil is delivered in a restrictive market dominated by unstable or hostile nations. Meanwhile, many Americans harbor misunderstandings and myths about energy, the environment, and market forces. They want low prices and plentiful supply, but resist steps that must be taken to achieve these goals. They want to protect the environment but most plans have huge costs and questionable benefits. This confusion leads Congress to enact conflicting policies that harm the nation's ability to meet its energy needs. Sound policies must enable America to obtain supplies from a wide range of sources in a way that is best for the economy and also addresses homeland and national security considerations.
1.To unleash the creativity of free enterprise to meet America's energy and environmental needs.
2.To protect America's energy interests.
3.To advance free global energy markets.
Slapping the word "rural" in front of a bunch of green subsidies doesn't mean they're not subsidies. But that's exactly what the Rural Energy Investment Act section of the Senate version of the farm bill legislation does.
Read More
It's been five years since TransCanada announced the Keystone XL expansion project, but we're still without a pipeline and the jobs and the energy that come with it because no presidential permit for construction has been authorized.
How the Administration is Holding Up Progress
All Americans want a clean, healthy, and safe environment. Yet an important question remains: Which public policies will realize our environmental goals most effectively? Heritage's Environmental Conservation explores the principles that should shape American environmental policy.
A bill to reform nuclear waste management in the U.S. does little to combat the current, failed system. Policies that introduce market forces and corporate responsibility are the key that could finally unleash the nuclear renaissance.
Say you were a politician and there was a clean and abundant domestic energy source — one that has the potential to create jobs and revitalize local economies. You'd do more to encourage it, right?
Is Nuclear Energy Safe? Can It Help Meet Our Energy Needs? Powering America answers these questions and more by pulling back the veil on nuclear energy.
Watch the Film Now
The Climate Protection Act of 2013 (Boxer–Sanders bill) would inflict high costs on families, and would be a step in the wrong direction for tax reform.
Although an international consensus addressing the issue of climate change has not been reached, the Obama Administration insists the U.S. carry the torch on this challenge through a series of unilateral actions. Even if it's detrimental to the economy.
Receive weekly updates on the ongoing policy battles in Washington and around the country.
Already Signed Up?
Manage your subscriptions
America's primary environmental goal should be a cleaner, healthier, and safer environment for current and future generations. Yet, governing environmental laws have strayed far from intended purposes, and their implementations are imposing immense costs on Americans with few benefits in return.
A complete disruption of Saudi oil production would significantly affect global energy markets and overall economic activity. The United States needs to prepare domestic and international measures before such a disruption occurs.
Posted March 13, 2014 by Nicolas Loris, Jack Spencer
Posted March 12, 2014 by Nicolas Loris
Posted March 4, 2014 by Nicolas Loris, Filip Jolevski
Posted February 4, 2014 by Nicolas Loris
Posted January 30, 2014 by Nicolas Loris
America has an energy addiction – and it's not an addiction to oil.
The administration's SunShot initiative is burning American taxpayers.
Posted October 28, 2013 by James Jay Carafano, Ph.D.
Posted October 28, 2013 by David W. Kreutzer, Ph.D.
Posted July 25, 2013 by Nicolas Loris
Posted June 27, 2013 by Nicolas Loris
Posted June 11, 2013 by Nicolas Loris, Katie Tubb
Posted September 20, 2013
Posted September 12, 2013
Posted July 8, 2013
Posted June 29, 2013
Posted May 30, 2013
Posted May 15, 2013
Posted February 27, 2013
Posted April 19, 2012
Posted April 3, 2012
Posted April 2, 2012
Posted September 6, 2013
Posted August 30, 2013
Posted August 13, 2013
Posted August 7, 2013
Posted August 6, 2013
Posted August 1, 2013
Posted July 31, 2013
Posted July 30, 2013
Jack Spencer oversees Heritage Foundation research on a wide range of domestic economic issues as director of the Roe Institute for Economic...
David Kreutzer is the Research Fellow in Energy Economics and Climate Change at The Heritage Foundation's Center for Data Analysis.
Nicolas (Nick) Loris, an economist, focuses on energy, environmental and regulatory issues as the Herbert and Joyce Morgan fellow at The Her...
Derrick D. Morgan leads one of the nation's most respected teams of public policy researchers and analysts as vice president for domesti...
Becky Norton Dunlop's duties and travel itinerary as The Heritage Foundation's vice president for external relations have made her the t...
As senior advisor for strategic outreach, Robert Gordon establishes and builds working relationships with new audiences and potential al...
Rediscovering our principles in order to reclaim our future.
Advancing American leadership and freedom in the world.
Strengthening American education by returning authority to the states and...
Eliminating barriers to enterprise and innovation.
Replacing the culture of entitlements with one of mutual responsibility.
To sustain freedom and the common good, family and religion are central
Providing every American freedom of choice in health care.
Protecting America and Americans from freedom's enemies.
Reestablishing the rule of law as the foundation for constitutional...
The U.S. and Japan share many interests in Southeast Asia, from development to military modernization. Where they can work together most productively and synergistically — Read more
In Reefer Sanity, Dr. Kevin Sabet considers the consequences of marijuana legalization. He uses a plethora of research – drawn from his almost two — Read more
Tweet this event using #EndExIm @Heritage and @SenMikeLee.Compounding America's crises of economic immobility and insecurity is the growing crisis of crony capitalism. — Read more
A centennial from the beginning of World War I is a grim reminder of just how important U.S. leadership in the world is. The — Read more
In this book, Haroon K. Ullah analyzes the origins, ideologies, bases of support, and electoral successes of the largest and most influential Islamic parties in — Read more
Every election, as presidential hopefuls jockey for the Republican nomination, each one claims to be a "Reagan conservative." But are these candidates truly carrying — Read more
In October 1986, Ronald Reagan and Mikhail Gorbachev met for a forty-eight-hour summit in Reykjavik, Iceland. Originally intended as a short, inconsequential gathering to — Read more
What does it mean for Iran to have nuclear weapons capabilities?And what should the United States do about this threatening situation? Iran's advanced nuclear program may — Read more
Drawing on examples from the nation's past and present, Uncle Sam Can't Count provides a thorough economic history of why the federal government cannot – — Read more
History has not ended. Across the world today, we are witnessing both a heroic struggle for democracy and reform and the disturbing strength of — Read more
The Heritage Foundation is the nation's most broadly supported public policy research institute, with hundreds of thousands of individual, foundation and corporate donors. Heritage, founded in February 1973, has a staff of 275 and an annual expense budget of $82.4 million.
Our mission is to formulate and promote conservative public policies based on the principles of free enterprise, limited government, individual freedom, traditional American values, and a strong national defense. Read More
Receive updates from Heritage about current events and initiatives in your email inbox
Already Signed up?
© 2014, The Heritage Foundation Conservative policy research since 1973


text:
You can view the current or previous issues of Diabetes Health online, in their entirety, anytime you want.
Click Here To View
See if you qualify for our free healthcare professional magazines. Click here to start your application for Pre-Diabetes Health, Diabetes Health Pharmacist and Diabetes Health Professional.
Latest Insulin Articles
Popular Insulin Articles
Highly Recommended Insulin Articles
Send a link to this page to your friends and colleagues.
I never thought I would weigh over 200 pounds in my life, yet I stepped on the scales one day and weighed 214 pounds!
Having type 1 diabetes for 16 years, I always knew that being obese was not a good thing for blood glucose control. I was also aware that weight gain is a common side effect of insulin therapy.
I vowed to lose the weight, so I consulted with my certified diabetes educator (CDE) and was started on a weight loss program that consisted of a 1500-calorie, low-fat diet and an exercise program. As my exercise level increased, we slowly adjusted my daily injections of long- and short-acting insulin. Despite the weight gain, my blood sugars had always been under control, with most readings less than 140 mg/dl and HbA1cs under 6%.
Dieting and exercising sent my blood sugars on a rollercoaster ride, and if I planned a day of heavy exercise, I would decrease my long-acting insulin by four units. For some reason, however, if I could not exercise on a certain day, my blood sugars would rise to 400 mg/dl. If I found extra time during the day to exercise, my blood sugars plummeted to 30 mg/dl and I would have to increase my carbohydrate intake. It became a frustrating and vicious cycle of high and low blood sugars, and my CDE couldn't find a happy medium.
One day, however, my CDE introduced me to the insulin pump. She felt it might help me to lose weight by offering me adjustments with insulin in tenths of an increment, and get me away from unpredictable, long-acting insulin. In June of 1998, I started wearing an insulin pump, and the first six months were trial and error. We would adjust my basal rates to an appropriate setting, only to readjust it a few days later.
During my adjustment period, I had daily contact with my physician and CDE. When I was finally adjusted to life on a pump, I was given the go-ahead by my CDE to start the low-fat, 1500-calorie ADA diet, and begin my exercise routine. I had to document every blood sugar reading, every piece of food I ate, and all my exercise activity.
To coincide with my exercise program, I programmed my pump to fit my very active lifestyle. On the days when I would wake up early to run, my pump was programmed to decrease the basal rate by 0.2 at 5 a.m. so that while I was running, my blood sugar would not bottom out. I would often walk after lunch, so my pump was programmed to decrease one hour prior to my activity. During my exercise, I decrease my basal rate by one-half, and if my hectic schedule or the weather puts a damper on my activities, I program my pump accordingly. I have been known to go on for days without time to exercise so, once again, I adjust my pump accordingly. None of this would be possible if I was still taking injections of long- and short-acting insulin.
Today I look and feel better than I have in years. My BMI has dropped from a very unhealthy 37 to a healthy 25. I now step on the scales at 142 pounds, and on days when I am feeling stressed out, I can exercise more easily to relieve the tension. The insulin pump has allowed me to enjoy life and become healthier at the same time.
Lisa Buntain is an insulin pump user who lives in Defiance, Ohio
Categories: A1c Test, Blood Glucose, Blood Sugar, Diabetes, Food, Insulin, Insulin Pumps, Losing weight, Type 1 Issues, Weight Loss
0 comments - Apr 1, 1999
Diabetes Health is the essential resource for people living with diabetes- both newly diagnosed and experienced as well as the professionals who care for them. We provide balanced expert news and information on living healthfully with diabetes. Each issue includes cutting-edge editorial coverage of new products, research, treatment options, and meaningful lifestyle issues.


text:
Our college is committed to provide high quality nursing education for students and to provide opportunities for life-long learning for nurses. The College of Nursing also recognizes the diversity and uniqueness of the community that it serves, and is committed to the enhancement of the quality of health care through excellence in teaching, research, service, continuing education and the promotion of evidence-based practice.
The University of Texas at Brownsville website uses Javascript.
To access this page as it is meant to function please use a Javascript
enabled browser.


text:
"Johnny We hardly knew ye"
Daniel Moore to further immortalize Johnny Football
Daniel Moore, for those of you unfamiliar with Tuscaloosa's Thomas Kinkade, has earned some measure of fame in SEC country for his oil-paint depictions of glorious moments in Crimson Tide football lore. This offseason, however, he'll take a rare detour from Rammer Jammerian subject matter to focus on a surprising new project: A portrait of Johnny Football besting Alabama in the Tide's only loss of 2012. (As the A&M loss didn't cost 'Bama a trip to the title game, we suppose he can afford to be gracious.)
Moore's paintings typically carry simple, sturdy titles, such as "Crimson Dynasty," "Maximum Block" or "The Winning Connection," and because it is Tuesday and we literally have nothing better to do, we set out to collect suggestions from our readership for possible titles to adorn this new work. Cast your vote for one of six submissions after the jump:
Like the Sooners said, you can protect against the "Run and Shoot", but you can't protect against Manziel. Gig'em
Whatever it's named, I hope it hangs in the Gig-Em-Heim Museum someday.
I don't know what to call it but the most dramatic play was in the opening drive of the game. It was 3rd and long. The crowd noice was deafening when Johnny broke through into sacred Tide territory. It was as gutsy a run that I ever seen.
The Devil Went Down to Tuscaloosa
The Persistence of Manziel-ory


text:
Narrow your search
About a year after Intuit's acquisition of Mint.com, it is closing the doors on its competing product. The technological migration has been tough for the company, it acknowledges.
The free personal finance iPhone app features very little in the way of security, but comes with ease of access.
An update to financial iPhone app Mint.com adds extra security, editing options, an a view to your budget.
Mint.com is adding new features to help its users keep track of their budget.
Tired of using the clunky bill-pay section of your bank's Web site? Manilla wants to take the pain out of paying your bills.
And now, too, there's also a Windows 8 version of Mint, the financial-planning and transaction-tracking service.
Aaron Patzer, now an Intuit vice president following its acquisition of Mint.com, is now faced with uniting two distinctly different personal-finance software brands.
Mint.com is doling out award points and achievements for smart financial decisions made by its users.
In Google's list of best apps for 2012, the company mentions some expected big hitters, as well as a few niche titles out of left field.
An Android app from personal finance service Mint.com joins its iPhone counterpart, though neither lives up to the full-fledged site.


text:
Bianchi 7916 AccuMold Elite Silent Key Holder Basketweave Nylon Black
Product #: 351504 Bianchi #: 22119 UPC #: 013527221193
Others Bought
Product Information
Bianchi's AccuMold Elite revolutionizes the concept of duty gear, crossing the boundaries between leather and nylon holsters and accessories. Even though it is constructed from Nylon, it retains the same crisp, professional appearance of premium leather without the weight, maintenance and cost. This improved comfort translates into better officer performance and safety in the field. Available in a wide range of holsters and accessories for any type of law enforcement assignment.
Technical Information
Material: Trilaminate
Style: Velcro closure
Standard Key Rings
The exclusive Roll-Top wraparound closure holds keys securely and silently
Fits belts 2-1/4" wide.
Technical Information
Material: Trilaminate
Style: Velcro closure
Know someone who may like this? Email this Page to a Friend
Customer Reviews
The statements and opinions expressed within Customer Reviews reflect each author's personal perspective and do not imply endorsement by MidwayUSA, its Employees or any other organization. Customers should in no way infer that MidwayUSA has validated review information as safe, accurate or appropriate.
NOTE: Prices, specifications and availability are subject to change without notice.
We reserve the right to correct typographic, photographic and/or descriptive errors.
We reserve the right to correct typographic, photographic and/or descriptive errors.