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

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 cluster...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 32; no. 3; pp. 1 - 15
Main Authors: Li, Hongzong, Wang, Jun
Format: Journal Article
Language:English
Published: IEEE 01.03.2024
Subjects:
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Annealing
annealing procedure
Classification algorithms
Clustering algorithms
Clustering methods
collaborative clustering
Cooling
fuzzy <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> c</tex-math> </inline-formula>-means clustering
Linear programming
Schedules
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary: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.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2023.3319663