Authors: Belacel, Nabil
Hansen, Pierre
Mladenović, Nenad 
Title: Fuzzy J-Means: A new heuristic for fuzzy clustering
Journal: Pattern Recognition
Volume: 35
Issue: 10
First page: 2193
Last page: 2200
Issue Date: 1-Jan-2002
Rank: M21
ISSN: 0031-3203
DOI: 10.1016/S0031-3203(01)00193-5
A fuzzy clustering problem consists of assigning a set of patterns to a given number of clusters with respect to some criteria such that each of them may belong to more than one cluster with different degrees of membership. In order to solve it, we first propose a new local search heuristic, called Fuzzy J-Means, where the neighbourhood is defined by all possible centroid-to-pattern relocations. The "integer" solution is then moved to a continuous one by an alternate step, i.e., by finding centroids and membership degrees for all patterns and clusters. To alleviate the difficulty of being stuck in local minima of poor value, this local search is then embedded into the Variable Neighbourhood Search metaheuristic. Results on five standard test problems from the literature are reported and compared with those obtained with the well-known Fuzzy C-Means heuristic. It appears that solutions of substantially better quality are obtained with the proposed methods than with this former one.
Keywords: Fuzzy C-Means | Fuzzy clustering | Local search | Unsupervised classification | Variable neighbourhood search
Publisher: Elsevier
Project: NSERC, Grant #GP0105574
FCAR, Grant #95ER1048

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