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A New Evolutionary Algorithm for Cluster Analysis

Authors: B.Bahmani Firouzi, T. Niknam, M. Nayeripour


Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.

Keywords: Data Clustering, Particle Swarm Optimization (PSO), K-means algorithm, Hybrid evolutionary optimization algorithm, Simulated Annealing (SA)

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