From Soft Clustering to Hard Clustering : A Collaborative Annealing Fuzzy c-means Algorithm

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Detail(s)

Original languageEnglish
Pages (from-to)1181-1194
Journal / PublicationIEEE Transactions on Fuzzy Systems
Volume32
Issue number3
Online published27 Sept 2023
Publication statusPublished - Mar 2024

Abstract

The fuzzy c-means clustering algorithm is the most widely used soft clustering algorithm. In contrast to hard clustering, the cluster membership of data generated using the fuzzy c-means algorithm is ambiguous. Similar to hard clustering algorithms, the clustering results of the fuzzy cmeans clustering algorithm are also sub-optimal with varied performance depending on initial solutions. In this paper, a collaborative annealing fuzzy c-means algorithm is presented. To address the issue of ambiguity, the proposed algorithm leverages an annealing procedure to phase out the fuzzy cluster membership degree toward a crispy one by reducing the exponent gradually according to a cooling schedule. To address the issue of sub-optimality, the proposed algorithm employs multiple fuzzy c-means modules to generate alternative clusters based on membership srepeatedly re-initialized using a meta-heuristic rule. Experimental results on eight benchmark datasets are elaborated to demonstrate the superiority of the proposed algorithm to thirteen prevailing hard and soft algorithms in terms of internal and external cluster validity indices. © 2023 IEEE.

Research Area(s)

  • fuzzy c-means clustering, Annealing, annealing procedure, Classification algorithms, Clustering algorithms, Clustering methods, collaborative clustering, Cooling, K-means clustering, Linear programming, Schedules