The automatic clustering algorithms are known to work well in dealing with clusters of regular shapes, e.g. compact spherical/elongated 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. 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.
Available at: http://works.bepress.com/keke_chen/2/