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Winter 2004, Volume 3, Number 4, Pages 257-270
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| Original Article |
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| VISTA: validating and refining clusters via visualization |
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| Keke Chen1 and Ling Liu1 |
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1College of Computing, Georgia Institute of Technology, GA, U.S.A.
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Correspondence to: Keke Chen, College of Computing, Georgia Institute of Technology, 801 Atlantic Dr., Atlanta, GA 30332, U.S.A. Tel: +1 404 633 6594; E-mail: kekechen@cc.gatech.edu |
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| Abstract |
| Clustering is an important technique for understanding of large multi-dimensional datasets. Most of clustering research to date has been focused on developing automatic clustering algorithms and cluster validation methods. The automatic algorithms are known to work well in dealing with clusters of regular shapes, for example, compact spherical shapes, but may incur higher error rates when dealing with arbitrarily shaped clusters. Although some efforts have been devoted to addressing the problem of skewed datasets, the problem of handling clusters with irregular shapes is still in its infancy, especially in terms of dimensionality of the datasets and the precision of the clustering results considered. Not surprisingly, the statistical indices works ineffective in validating clusters of irregular shapes, too. In this paper, we address the problem of clustering and validating arbitrarily shaped clusters with a visual framework (VISTA). The main idea of the VISTA approach is to capitalize on the power of visualization and interactive feedbacks to encourage domain experts to participate in the clustering revision and clustering validation process. The VISTA system has two unique features. First, it implements a linear and reliable visualization model to interactively visualize multi-dimensional datasets in a 2D star-coordinate space. Second, it provides a rich set of user-friendly interactive rendering operations, allowing users to validate and refine the cluster structure based on their visual experience as well as their domain knowledge.
Information Visualization (2004) 3, 257-270. doi:10.1057/palgrave.ivs.9500076
Published online 22 July 2004 |
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| Keywords |
| data clustering; cluster analysis framework; interactive cluster visualization; cluster validation and refining |
| Received 6 November 2003; revised 9 March 2004; accepted 31 March 2004; published online 22 July 2004 |
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