Journal ArticleDOI
A new cluster validity measure and its application to image compression
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TLDR
This paper proposes a new validity measure that can deal with the edge degradation in vector quantisation of image compression and proposes a modified K-means algorithm that can assign more cluster centres to areas with low densities of data.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.read more
Citations
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An extensive comparative study of cluster validity indices
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Automatic Clustering Using an Improved Differential Evolution Algorithm
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A survey on nature inspired metaheuristic algorithms for partitional clustering
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Cluster validity measurement techniques
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References
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