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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/611
Title: A survey on nature inspired metaheuristic algorithms for partitional clustering
Authors: Nanda S.J.
Panda G.
Keywords: Evolutionary algorithms
Multi-objective Clustering
Nature inspired metaheuristics
Partitional clustering
Swarm intelligence
Issue Date: 2014
Citation: 219
Abstract: The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
URI: http://dx.doi.org/1016/j.swevo.2013.11.003
http://10.10.32.48:8080/jspui/handle/2008/611
Appears in Collections:Research Publications

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