Study of different approach to clustering data by using the Particle Swarm Optimization Algorithm

This paper proposes two new data clustering approaches using the particle swarm optimization algorithm (PSO). It is shown how the PSO can be used to find centroids of a user specified number of clusters. The proposed approaches are an attempt to improve the Merwe and Engelbrecht method using different fitness functions and considering the situation where data is uniformly distributed. The data clustering PSO algorithm, using the original and proposed fitness functions is evaluated on well known data sets. Notable improvements on the results were achieved by the modifications, this shows the potential of the PSO, not only on data clustering but also on the several areas it can be applied.

[1]  Pasi Fränti,et al.  Branch-and-bound technique for solving optimal clustering , 2002, Object recognition supported by user interaction for service robots.

[2]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[3]  Leandro N. de Castro,et al.  Data Clustering with Particle Swarms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.