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In this paper, a similarity-driven cluster merging method is proposed forunsuper-vised fuzzy clustering. The cluster merging method is used to resolvethe problem of cluster validation. Starting with an overspecified number ofclusters in the data, pairs of similar clusters are merged based on theproposed similarity-driven cluster merging criterion. The similarity betweenclusters is calculated by a fuzzy cluster similarity matrix, while an adaptivethreshold is used for merging. In addition, a modified generalized ob- jectivefunction is used for prototype-based fuzzy clustering. The function includesthe p-norm distance measure as well as principal components of the clusters.The number of the principal components is determined automatically from thedata being clustered. The properties of this unsupervised fuzzy clusteringalgorithm are illustrated by several experiments.

Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering