A Fuzzy Genetic Clustering Technique Using a New Symmetry Based Distance for Automatic Evolution of Clusters

Authors:
Sriparna Saha;Sanghamitra Bandyopadhyay
Affiliations:
Indian Statistical Institute, India;Indian Statistical Institute, India
Venue:
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Year:
2007

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High dimensional data clustering through fuzzy possibilistic C-means with symmetry-based distance measure

International Journal of Computational Intelligence Studies

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Abstract

In this paper a fuzzy point symmetry based genetic clustering technique (Fuzzy-VGAPS) is proposed which can determine the number of clusters present in a data set as well as a good fuzzy partitioning of the data. A new fuzzy cluster validity index, FSym-index, which is based on the newly developed point symmetry based distance is also proposed here. FSym-index provides a measure of goodness of clustering on different fuzzy partitions of a data set. Maximum value of FSym-index corresponds to the proper clustering present in a data set. The flexibility of Fuzzy-VGAPS is utilized in conjunction with the fuzzy FSym-index to determine the number of clusters present in a data set as well as a good fuzzy partition of the data. The results of the fuzzy VGAPS are compared with those obtained by fuzzy version of variable string length genetic clustering technique (Fuzzy-VGA) optimizing XB-index. The effectiveness of the Fuzzy-VGAPS is demonstrated on four artificial data sets and two real-life data sets.