Pattern Analysis and Applications

, Volume 7, Issue 2, pp 205–220 | Cite as

A new cluster validity measure and its application to image compression

  • C.-H. Chou
  • M.-C. Su
  • E. Lai
Theoretical Advances


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.


Clustering algorithm Cluster validity Image compression Pattern recognition Vector quantisation 



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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Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  1. 1.Institute of Information ScienceAcademia SinicaTaipeiTaiwan, R.O.C.
  2. 2.Department of Computer Science & Information EngineeringNational Central UniversityTaiwan, R.O.C.
  3. 3.Department of Electrical EngineeringTamkang UniversityTaiwan, R.O.C.

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