DICON

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. To address this challenge, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. DICON employs 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. An evaluation of our visualization design has confirmed benefits of the technique, especially in support of complex multidimensional cluster analysis.

Publications

David Gotz, Jimeng Sun, Nan Cao.  Multifaceted Visual Analytics for Healthcare Applications.  IBM Journal of Research and Development (Volume 56, Number 5, 2012).
[This article is available through the IEEE Digital Library.  View the article by clicking here.] 

David Gotz, Jimeng Sun, Nan Cao, and Shahram Ebadollahi. Visual Cluster Analysis in Support of Clinical Decision Intelligence. American Medical Informatics Association Annual Symposium (AMIA), Washington, DC (2011).
[PDF, 1.3M]

Nan Cao, David Gotz, Jimeng Sun, and Huamin Qu. DICON: Interactive Visual Analysis of Multidimensional Clusters. IEEE Information Visualization (InfoVis), Providence, Rhode Island (2011).
[PDF, 6.7M]

An Alternative Citation: Nan Cao, David Gotz, Jimeng Sun, and Huamin Qu. DICON: Interactive Visual Analysis of Multidimensional Clusters. IEEE Transactions on Visualization and Computer Graphics (Volume 17, Number 12, 2011).
[This article is available through the IEEE Digital Library. View the article by clicking here.]