Title: Estimating cluster validity using compactness measure and overlap measure for fuzzy clustering

Authors: Bindu Rani; Shri Kant

Addresses: Sharda University, Greater Noida, Uttar Pradesh 201310, India ' Sharda University, Greater Noida, Uttar Pradesh 201310, India

Abstract: Cluster analysis discovers valuable patterns in data by partitioning n data points into valid number of clusters. The cluster validity index (CVI) helps in selecting the best partitions that fits the underlying structure of data. After presenting brief review on existing CVIs, this study formulates a competent overlap-compactness validity index (OCVI). The proposed index considers Kim et al.'s (2004b) overlap measure with compactness measure. Compactness measure considers the geometrical aspects of membership matrix (U) through cluster centres with an approach to reduce its monotonic tendency. Overlap measure calculates the average value of the overlapping degree of all probable fuzzy cluster pairs. Experiments are implemented on two artificial, two real and one biological dataset. Comparison results of partition coefficient, partition entropy, modified partition coefficient, Xie-Beni and Kim indices with the suggested index (OCVI) imply that suggested index outperforms with maximum compactness and minimum overlap than other validity indices.

Keywords: cluster validity index; CVI; clustering; fuzzy clustering; fuzzy c-means algorithm.

DOI: 10.1504/IJBIDM.2022.122158

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.3, pp.345 - 363

Received: 24 Mar 2020
Accepted: 07 Oct 2020
Published online: 11 Apr 2022 *

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