Rough Clustering Method Based on Particle Swarm Optimization Algorithm

Abstract:

As a relatively novel clustering approach, Particle Swarm Optimization (PSO) prevents k-means algorithm from falling into local optimum effectively, and has made relatively notable successes in clustering, however, using Hard C-Means algorithm when randomly obtaining initial clustering centers is required in most existing PSOs, while no definite limit existing in these samples actually. Based on this, we utilized an improved PSO; along with effective processing methods on boundary objects of Rough Set Theory, we proposed a new rough clustering algorithm based on PSO. It can adjust the upper and lower approximations weighting factors dynamically, and coordinate the proportions of upper and lower approximations in different generations as well. Finally, we compared it with several common clustering methods using Iris dataset of UCI. It turned out that the algorithm has higher accuracy and stability, along with better comprehensive performance.

• Periodical: Applied Mechanics and Materials (Volumes 325-326)
• Pages: 1632-1636
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.325-326.1632
• Online since: June 2013
• Authors: Chao Wang, Ke Luo
• Keywords: Cluster, K-Mean, Particle Swarm Optimization Algorithm, Rough Set (RS)

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