A new cluster validity measure and its application to image compression View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-07

AUTHORS

C.-H. Chou, M.-C. Su, E. Lai

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. More... »

PAGES

205-220

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10044-004-0218-1

DOI

http://dx.doi.org/10.1007/s10044-004-0218-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012721897


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