anchor.diff:  VAST,TVCG,Analyzing the Training Processes of Deep Generative Models,"Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu",,,,,,,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,10:30 AM,10:50 AM
   VAST,TVCG,Applying Pragmatics Principles for Interaction with Visual Analytics,"Enamul Hoque, Vidya Setlur, Melanie Tory, Isaac Dykeman",https://research.tableau.com/sites/default/files/VAST2017_105.pdf,"Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools.",,,,vimeo 230831885,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:40 PM,3:00 PM
   VAST,TVCG,Beyond Tasks: An Activity Typology for Visual Analytics,"Darren Edge, Nathalie Henry Riche, Jonathan Larson, Christopher White",,,,,,,,2018,2017,VAST,301-C,Thursday,Theory and Analysis Process,10:30 AM,10:50 AM
  -VAST,TVCG,BiDots: Visual Exploration of Weighted Biclusters,"Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu",,,,,,,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:35 PM,4:55 PM
  -VAST,TVCG,Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,"Manuel Stein, Halldór Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlücke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel Keim",,,,,,,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:00 PM,2:20 PM
  -VAST,TVCG,Clustering Trajectories by Relevant Parts for Air Traffic Analysis,"Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia",,,,,,,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:40 PM,3:00 PM
  +VAST,TVCG,BiDots: Visual Exploration of Weighted Biclusters,"Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu",,,,,,youtube 79VSgVIl11M,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:35 PM,4:55 PM
  +VAST,TVCG,Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,"Manuel Stein, Halldór Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlücke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel Keim",,,,,,vimeo 230830922,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:00 PM,2:20 PM
  +VAST,TVCG,Clustering Trajectories by Relevant Parts for Air Traffic Analysis,"Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia",http://openaccess.city.ac.uk/18119/1/main-reducedSize.pdf,"Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.",,,,vimeo 227918021,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:40 PM,3:00 PM
   VAST,TVCG,Clustervision: Visual Supervision of Unsupervised Clustering,"Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter Stewart, Adam Perer",http://perer.org/papers/adamPerer-Clustervision-VAST2017.pdf,"Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built <em>Clustervision</em>, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.",,,,vimeo 232177941,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,5:15 PM,5:35 PM
  -VAST,TVCG,Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study,"Jürgen Bernard, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, Michael Sedlmair",,,,,,,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:20 PM,2:40 PM
  +VAST,TVCG,Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study,"Jürgen Bernard, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, Michael Sedlmair",http://homepage.univie.ac.at/michael.sedlmair/papers/bernard2017labeling.pdf,"Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.",,,,video http://homepage.univie.ac.at/michael.sedlmair/papers/bernard2017labeling.mp4,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:20 PM,2:40 PM
   VAST,TVCG,ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding,"Deokgun Park, Seungyeon Kim, Jurim Lee, Jaegul Choo, Nicholas Diakopoulos, Niklas Elmqvist",http://www.umiacs.umd.edu/~elm/projects/conceptvector/conceptvector.pdf,"Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building such concepts from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of human language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides the user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts using user seed terms, we introduce a bipolar concept model and support for irrelevant words. We validate the interactive lexicon building interface via a user study and expert reviews. The quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.",,,,youtube -_gANFu-ulY,,2018,2017,VAST,207,Wednesday,Text Analytics,11:10 AM,11:30 AM
   VAST,TVCG,DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks,"Nicola Pezzotti, Thomas Höllt, Jan van Gemert, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova",https://graphics.tudelft.nl/Publications-new/2018/PHVLEV18/paper216.pdf,"Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.",,,,vimeo 230830385,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,11:30 AM,11:50 AM
   VAST,TVCG,Do Convolutional Neural Networks learn Class Hierarchy?,"Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren",http://cvlab.cse.msu.edu/pdfs/DoConvolutionalNeuralNetworksLearnClassHierarchy.pdf,"Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.",,,,vimeo 230831852,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:30 AM,8:50 AM
  
positive.code: VAST,TVCG,EVA: Visual Analytics to Identify Fraudulent Events,"Roger Leite, Theresia Gschwandtner, Silvia Miksch, Simone Kriglstein, Margit Pohl, Erich Gstrein, Johannes Kuntner",,,,,,,,2018,2017,VAST,207,Thursday,Sensemaking,4:35 PM,4:55 PM
  VAST,TVCG,EventThread: Visual Summarization and Stage Analysis of Event Sequence Data,"Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2013/10/PREPRINT-Guo-VIS-2017-EventThread-Preprint.pdf,"Event sequence data such as electronic health records, a person’s academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.",,,,vimeo 230831051,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:20 PM,2:40 PM
  VAST,TVCG,Graphiti: Interactive Specification of Attribute-based Edges for Network Modeling and Visualization,"Arjun Srinivasan, Hyunwoo Park, Alex Endert, Rahul Basole",http://arjun010.github.io/static/papers/graphiti-vast-17.pdf,"Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.",,,,vimeo 230830104,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,5:35 PM,5:55 PM
  VAST,TVCG,How Do Ancestral Traits Shape Family Trees over Generations?,"Siwei Fu, Hao Dong, Weiwei Cui, Jian Zhao, Huamin Qu",,,,,,,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:55 PM,5:15 PM
  VAST,TVCG,LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets,"Jiazhi Xia, Fenjin Ye, Wei Chen, Yusi Wang, Weifeng Chen, Yuxin Ma, Anthony K. H. Tung",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/LDSScanner.pdf,"Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.",,,,vimeo 230830366,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,8:30 AM,8:50 AM
  VAST,TVCG,PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models,"Michael Glueck, Mahdi Pakdaman Naeini, Finale Doshi-Velez, Fanny Chevalier, Azam Khan, Daniel Wigdor, Michael Brudno",,,,,,,,2018,2017,VAST,207,Wednesday,Text Analytics,11:30 AM,11:50 AM
  VAST,TVCG,Podium: Ranking Data Using Mixed-Initiative Visual Analytics,"Emily Wall, Subhajit Das, Ravish Chawla, Bharath Kalidindi, Eli T. Brown, Alex Endert",https://www.cc.gatech.edu/~ewall9/media/papers/PodiumVAST17.pdf,"People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user’s data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user’s subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings.Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas",,,,youtube Z4N9wKxK2i4,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:00 PM,2:20 PM
  
negative.code: InfoVis,TVCG,Nonlinear Dot Plots,"Nils Rodrigues, Daniel Weiskopf",,,,,,,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:40 PM,3:00 PM
  InfoVis,TVCG,Open vs Closed Shapes: New Perceptual Categories?,"David Burlinson, Kalpathi Subramanian, Paula Goolkasian",,,,,,,,2018,2017,InfoVis,301-D,Wednesday,Perception,8:50 AM,9:10 AM
  InfoVis,TVCG,Orko: Facilitating Multimodal Interaction for Visual Network Exploration and Analysis,"Arjun Srinivasan, John Stasko",http://arjun010.github.io/static/papers/orko-infovis-17.pdf,"Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques. We hypothesize that truly multimodal interfaces for visualization, those providing users with freedom of expression via both natural language and touch-based direct manipulation input, may provide an effective and engaging user experience. Unfortunately, however, little work has been done in exploring such multimodal visualization interfaces. To address this gap, we have created an architecture and a prototype visualization system called Orko that facilitates both natural language and direct manipulation input. Specifically, Orko focuses on the domain of network visualization, one that has largely relied on WIMP-based interfaces and direct manipulation interaction, and has little or no prior research exploring natural language interaction. We report results from an initial evaluation study of Orko, and use our observations to discuss opportunities and challenges for future work in multimodal network visualization interfaces.",,,,vimeo 230840776,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,5:15 PM,5:35 PM
  InfoVis,TVCG,Priming and Anchoring Effects in Visualizations,"André Calero Valdez, Martina Ziefle, Michael Sedlmair",http://homepage.univie.ac.at/michael.sedlmair/papers/calero-valdez2017priming.pdf,"We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that—under certain circumstances—such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.",,,,youtube ynD_mbygJdU,,2018,2017,InfoVis,301-D,Wednesday,Perception,9:30 AM,9:50 AM
  InfoVis,TVCG,Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization,"Yunhai Wang, Yanyan Wang, Yingqi Sun, Lifeng Zhu, Chi-Wing Fu, Michael Sedlmair, Oliver Deussen, Baoquan Chen, Kecheng Lu",http://graphics.uni-konstanz.de/publikationen/Wang2018RevisitingStressMajorization/constrained-graph.pdf,"We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes – an approach which previous methods cannot support.",,,,vimeo 230840913,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,4:35 PM,4:55 PM
  InfoVis,TVCG,"Scatterplots: Tasks, Data, and Designs","Alper Sarikaya, Michael Gleicher",http://graphics.cs.wisc.edu/Papers/2018/SG18/scatterplots-preprint.pdf,"Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots",,,,vimeo 230841068,,2018,2017,InfoVis,301-D,Wednesday,Design,10:30 AM,10:50 AM
  InfoVis,TVCG,Skeleton-based Scagnostics,"José Matute, Alex Telea, Lars Linsen",http://www.cs.rug.nl/~alext/PAPERS/InfoVis17/paper2.pdf,"Scatterplot matrices (SPLOMs) are widely used for exploring multidimensional data. Scatterplot diagnostics (scagnostics) approaches measure characteristics of scatterplots to automatically find potentially interesting plots, thereby making SPLOMs more scalable with the dimension count. While statistical measures such as regression lines can capture orientation, and graph-theoretic scagnostics measures can capture shape, there is no scatterplot characterization measure that uses both descriptors. Based on well-known results in shape analysis, we propose a scagnostics approach that captures both scatterplot shape and orientation using skeletons (or medial axes). Our representation can handle complex spatial distributions, helps discovery of principal trends in a multiscale way, scales visually well with the number of samples, is robust to noise, and is automatic and fast to compute. We define skeleton-based similarity metrics for the visual exploration and analysis of SPLOMs. We perform a user study to measure the human perception of scatterplot similarity and compare the outcome to our results as well as to graph-based scagnostics and other visual quality metrics. Our skeleton-based metrics outperform previously defined measures both in terms of closeness to perceptually-based similarity and computation time efficiency.",,,,vimeo 230841215,,2018,2017,InfoVis,301-D,Wednesday,Multidimensional Data,2:20 PM,2:40 PM
  