A New Cluster Validity Measure for Clusters with Different Densities

C.-H. Chou, M.-C. Su, and E. Lai (Taiwan)


cluster validity, cluster algorithm, pattern recognition, data mining


Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure, which can deal with this situation. The performance evaluation of the validity measure compares favorably to that of several validity functions and shows the effectiveness.

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