Nan Cao

Dynamic ICON (DICON) for Interactive Multidimensional Cluster Analysis

Screen Capture Demo

Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.

Papers:
1. DICON : Interactive Visual Analysis of Multidimensional Clusters (IEEE InfoVis 2011) (paper|ppt)
2. DICON: Visual Cluster Analysis in Support of Clinical Decision Intelligence. (AMIA 2011)
Patent:
1. YOR920110279US1 Visual Analysis of Multidimensional Clusters, Nan Cao, David Gotz, Jimeng Sun

Online Demo:

• drag the title bar of the icons to merge them together
• double click on the title bar to split the icons into pieces