A new cluster validity measure and its application to image compression

Authors:
C.-H. Chou;M.-C. Su;E. Lai
Affiliations:
Academia Sinica, Institute of Information Science, Taipei, Taiwan, R.O.C.;National Central University, Department of Computer Science & Information Engineering, Taipei, Taiwan, R.O.C.;Tamkang University, Department of Electrical Engineering, Taipei, Taiwan, R.O.C.
Venue:
Pattern Analysis & Applications
Year:
2004

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Abstract

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 that can deal with this situation. In addition, we also propose a modified K-means algorithm that can assign more cluster centres to areas with low densities of data than the conventional K-means algorithm does. First, several artificial data sets are used to test the performance of the proposed measure. Then the proposed measure and the modified K-means algorithm are applied to reduce the edge degradation in vector quantisation of image compression.