Visual Clutter Reduction through Hierarchy-based Projection of High-dimensional Labeled Data

Visualizing high-dimensional labeled data on a two-dimensional plane can quickly result in visual clutter and information overload. To address this problem, the data usually needs to be structured, so that only parts of it are displayed at a time. We present a hierarchy-based approach that projects labeled data on different levels of detail on a two-dimensional plane, whilst keeping the user's cognitive load between the level changes as low as possible. The approach consists of three steps: First, the data is hierarchically clustered; second, the user can determine levels of detail; third, the levels of detail are visualized one at a time on a two-dimensional plane. Animations make transitions between the levels of detail traceable, while the exploration on each level is supported by several interaction techniques. We demonstrate the applicability and usefulness of the approach with use cases from the patent domain and a question-and-answer website.

[1]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[2]  Ben Shneiderman,et al.  Tree visualization with tree-maps: 2-d space-filling approach , 1992, TOGS.

[3]  Thomas Ertl,et al.  TreeQueST: A Treemap-Based Query Sandbox for Microdocument Retrieval , 2015, 2015 48th Hawaii International Conference on System Sciences.

[4]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Tamara Munzner,et al.  Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists , 2014, IEEE Transactions on Visualization and Computer Graphics.

[6]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[7]  Steffen Lohmann,et al.  Comparison of Tag Cloud Layouts: Task-Related Performance and Visual Exploration , 2009, INTERACT.

[8]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[9]  Wolfgang Kienreich,et al.  On the Beauty and Usability of Tag Clouds , 2008, 2008 12th International Conference Information Visualisation.

[10]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

[11]  Michel Verleysen,et al.  Stability Comparison of Dimensionality Reduction Techniques Attending to Data and Parameter Variations , 2013, VAMP@EuroVis.

[12]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[13]  Patrick Baudisch,et al.  Halo: a technique for visualizing off-screen objects , 2003, CHI '03.

[14]  Qi Han,et al.  Visual Exploration of Patent Collections with IPC Clouds , 2014, IPaMin@KONVENS.

[15]  William Ribarsky,et al.  HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies , 2013, IEEE Transactions on Visualization and Computer Graphics.

[16]  Christian Posse,et al.  IN-SPIRE InfoVis 2004 Contest Entry , 2004, IEEE Symposium on Information Visualization.

[17]  Ye Zhao,et al.  Real-Time Visualization of Streaming Text with a Force-Based Dynamic System , 2012, IEEE Computer Graphics and Applications.

[18]  Michael Burch,et al.  Prefix Tag Clouds , 2013, 2013 17th International Conference on Information Visualisation.

[19]  Vincent Ng,et al.  Automatic Keyphrase Extraction: A Survey of the State of the Art , 2014, ACL.

[20]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[21]  Rosane Minghim,et al.  Semantic Wordification of Document Collections , 2012, Comput. Graph. Forum.

[22]  Thomas Ertl,et al.  Word Cloud Explorer: Text Analytics Based on Word Clouds , 2014, 2014 47th Hawaii International Conference on System Sciences.

[23]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[24]  Emden R. Gansner,et al.  Improved Force-Directed Layouts , 1998, GD.

[25]  Daniel Fried,et al.  Maps of Computer Science , 2013, 2014 IEEE Pacific Visualization Symposium.

[26]  Baining Guo,et al.  TopicPanorama: A Full Picture of Relevant Topics , 2014, IEEE Transactions on Visualization and Computer Graphics.

[27]  Andreas Butz,et al.  TagClusters: Semantic Aggregation of Collaborative Tags beyond TagClouds , 2009, Smart Graphics.

[28]  Haim Levkowitz,et al.  Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping , 2008, IEEE Transactions on Visualization and Computer Graphics.

[29]  Yee Whye Teh,et al.  Bayesian Rose Trees , 2010, UAI.

[30]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[31]  Jean-Daniel Fekete,et al.  ProxiLens: Interactive Exploration of High-Dimensional Data using Projections , 2013, VAMP@EuroVis.

[32]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[33]  Masahiro Ueno,et al.  A Clustering Method Using Hierarchical Self-Organizing Maps , 2002, J. VLSI Signal Process..

[34]  Owen Kaser,et al.  Tag-Cloud Drawing: Algorithms for Cloud Visualization , 2007, ArXiv.

[35]  Pak Chung Wong,et al.  Discovering Knowledge Through Visual Analysis , 2001, J. Univers. Comput. Sci..

[36]  Yusef Hassan-Montero,et al.  Improving Tag-Clouds as Visual Information Retrieval Interfaces , 2024, 2401.04947.

[37]  Jean-Daniel Fekete,et al.  Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines , 2010, IEEE Transactions on Visualization and Computer Graphics.

[38]  Ponnuthurai N. Suganthan Hierarchical overlapped SOM's for pattern classification , 1999, IEEE Trans. Neural Networks.

[39]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .