clustering models can be of different types on the basis of clustering methodologies and principles we will briefly introduce the different types of clustering algorithmswhich are as follows partition based clusteringa partition based clustering method is the most natural way to imagine the process of clustering partition based clustering method will define notion of similarity this can be any measure that can be derived from the attributes of the data points by applying mathematical functions on these attributes (featuresthenon the basis of this similarity measurewe can group data points that are similar to each other in single group and separate the ones that are different partition based clustering model is usually developed using recursive techniquei we start with some arbitrary partitions of data andbased on the similarity measurewe keep reassigning data points until we reach stable stopping criteria examples include techniques like -meansk-medoidsclaransand so on hierarchical clusteringa hierarchical clustering model is different from the partition based clustering model in the way they are developed and the way they work in hierarchical clustering paradigmwe either start with all the data points in one group (divisive clusteringor all the data points in different groups (agglomerativebased on the starting point we can either keep dividing the big group into smaller groups or clusters based on some accepted similarity criteria or we can keep merging different groups or clusters into bigger ones based on the same criteria this process is normally stopped when decided stopping condition is achieved similarity criteria could be inter-data point distance in cluster as compared to other cluster data points examples include ward' minimum variance criterion based agglomerative hierarchical clustering density based clusteringboth the clustering models mentioned previously are quite dependent on the notion of distance this leads to these algorithms primarily finding out spherical clusters of data this can become problem when we have arbitrary shaped clusters in the data this limitation can be addressed by doing away with the concept of distance metric based clustering we can define notion of "densityof data and use that to develop our cluster the cluster development methodology then changes from finding points in the vicinity of some points to finding areas where we have some data points this approach is not as straightforward to interpret as the distance metric approach but it leads to clusters that necessarily need not be spherical this is very desirable trait as it is unlikely that all the clusters of interest will be spherical in shape examples include dbscan and optics learning model we have been talking about building modelslearning parametersand so onsince the very start of this in this sectionwe explain what we actually mean by the term building model from the perspective of machine learning in the following sectionwe briefly discuss the mathematical aspects of learning model by taking specific model as an example to make things clearer we try to go light on the math in this sectionso that you don' get overwhelmed with excess information howeverinterested readers are recommended to check out any standard book on theoretical and conceptual details of machine learning models and their implementations (we recommend an introduction to statistical learning by tibshirani et al