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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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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DOI: https://doi.org/10.1007/s10462-010-9191-9