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Genetic algorithms for clustering and fuzzy clustering
Author(s) -
Bandyopadhyay Sanghamitra
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.47
Subject(s) - cluster analysis , fuzzy clustering , data mining , computer science , cure data clustering algorithm , correlation clustering , fuzzy logic , constrained clustering , canopy clustering algorithm , algorithm , machine learning , artificial intelligence
Clustering has been an area of intensive research for several decades because of its multifaceted applications in innumerable domains. Clustering can be either Boolean, where a single data point belongs to exactly one cluster, or fuzzy, where a single data point can have nonzero belongingness to more than one cluster. Traditionally, optimization of some well‐defined objective function has been the standard approach in both clustering and fuzzy clustering. Hence, researchers have investigated the utility of evolutionary computing and related techniques in this regard. The different approaches differ in their choice of the objective function and/or the optimization strategy used. In particular, clustering using genetic algorithms (GAs) has attracted attention of researchers, and has been studied extensively. This paper presents a short review of some of different approaches of GA‐based clustering methods. Two techniques, one with fixed number of clusters and another with a variable number of fuzzy clusters, are described along with some experimental results on numerical as well as image data sets. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 524–531 DOI: 10.1002/widm.47 This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Structure Discovery and Clustering