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A new cluster validity measure and its application to image compression

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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.

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Acknowledgement

This work is partially supported by the National Science Council, Taiwan, R.O.C, under the NSC 92-2213-E-008-008 and by the MOE Program for Promoting Academic Excellence of Universities under the grant number EX-91-E-FA06-4-4.

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Authors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C.

    C.-H. Chou

  2. Department of Computer Science & Information Engineering, National Central University, Taiwan, R.O.C.

    M.-C. Su

  3. Department of Electrical Engineering, Tamkang University, Taiwan, R.O.C.

    E. Lai

Authors
  1. C.-H. Chou
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  2. M.-C. Su
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  3. E. Lai
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Correspondence to C.-H. Chou.

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Chou, CH., Su, MC. & Lai, E. A new cluster validity measure and its application to image compression. Pattern Anal Applic 7, 205–220 (2004). https://doi.org/10.1007/s10044-004-0218-1

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