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Soft Computing

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01.05.2014 | Methodologies and Application

A hybrid clustering algorithm based on PSO with dynamic crossover

verfasst von: Jie Zhang, Yuping Wang, Junhong Feng

Erschienen in: Soft Computing | Ausgabe 5/2014

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Abstract

In order to overcome the premature convergence in particle swarm optimization (PSO), we introduce dynamical crossover, a crossover operator with variable lengths and positions, to PSO, which is briefly denoted as CPSO. To get rid of the drawbacks of only finding the convex clusters and being sensitive to the initial points in \(k\)-means algorithm, a hybrid clustering algorithm based on CPSO is proposed. The difference between the work and the existing ones lies in that CPSO is firstly introduced into \(k\)-means. Experimental results performing on several data sets illustrate that the proposed clustering algorithm can get completely rid of the shortcomings of \(k\)-means algorithms, and acquire correct clustering results. The application in image segmentation illustrates that the proposed algorithm gains good performance.

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Titel: A hybrid clustering algorithm based on PSO with dynamic crossover
verfasst von: Jie Zhang
Yuping Wang
Junhong Feng
Publikationsdatum: 01.05.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 5/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1115-6

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