Generalized agglomerative fuzzy clustering

Authors:
Kiatichai Treerattanapitak;Chuleerat Jaruskulchai
Affiliations:
Department of Computer Science, Kasetsart University, Chatuchak, Bangkok, Thailand;Department of Computer Science, Kasetsart University, Chatuchak, Bangkok, Thailand
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

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Abstract

Fuzzy Cluster is a powerful for cluster analysis. However, inappropriate parameters selection leads Fuzzy Clustering to produce unreliable results. In addition, Fuzzy Clustering is sensitive to initialization and could be struck in local minima. Although, clustering results are validated by Cluster Validity Index but these methods obtain the best clustering result by reproduce clustering with various parameters and it is computation expensive. In order to overcome these issues, Generalized Agglomerative Fuzzy Clustering is proposed in this paper. Our proposed method is capable to find the optimum number of clusters and fuzzifier during the clustering execution. Moreover, this method is applicable to Fuzzy Clustering and its variants. Comprehensive experiments show that our agglomerative method obtained the right number of clusters and fuzzifier.