Partitional Algorithms for Hard Clustering Using Evolutionary and Swarm Intelligence Methods: A Survey

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)

Abstract

Evolutionary and swarm intelligence methods attracted attention and gained popularity among the data mining researchers due to their expedient implementation, parallel nature, ability to search global optima and other advantages over conventional techniques. These methods along with their variants and hybrid approaches have emerged as worthwhile class of methods for clustering. Clustering is an unsupervised classification method. The partitional clustering algorithms look for hard clustering; they decompose the dataset into a set of disjoint clusters. This paper describes a brief review of evolutionary and swarm intelligence methods with their variants and hybrid approaches designed for partitional clustering algorithms for hard clustering of datasets.

Keywords

Evolutionary algorithm Swarm intelligence Clustering Unsupervised classification 

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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Computational Intelligence and Data Mining Research LabABV-Indian Institute of Information Technology and ManagementGwaliorIndia

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