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A review on particle swarm optimization algorithms and their applications to data clustering

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Abstract

Data clustering is one of the most popular techniques in data mining. It is a method of grouping data into clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The most popular clustering algorithm K-mean and other classical algorithms suffer from disadvantages of initial centroid selection, local optima, low convergence rate problem etc. Particle Swarm Optimization (PSO) is a population based globalized search algorithm that mimics the capability (cognitive and social behavior) of swarms. PSO produces better results in complicated and multi-peak problems. This paper presents a literature survey on the PSO application in data clustering. PSO variants are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of PSO and data clustering.

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Authors and Affiliations

  1. School of ICT, Gautam Buddha University, Greater Noida, India

    Sandeep Rana & Sanjay Jasola

  2. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India

    Rajesh Kumar

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  1. Sandeep Rana
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  2. Sanjay Jasola
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Correspondence to Sandeep Rana.

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Rana, S., Jasola, S. & Kumar, R. A review on particle swarm optimization algorithms and their applications to data clustering. Artif Intell Rev 35, 211–222 (2011). https://doi.org/10.1007/s10462-010-9191-9

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