Abstract
Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Particle Swarm Optimization is a stochastic global optimization technique that is the proper solution to solve this problem. In this paper, PSOKHM not only helps KHM clustering escape from local optima but also overcomes the shortcoming of slow convergence speed of PSO. In this paper, a hybrid data clustering algorithm based on PSO and Genetic algorithm, GSOKHM, is proposed. We investigate local optima method in addition to the global optima in PSO, called LSOKHM. The experimental results on five real datasets indicate that LSOKHM is superior to the GSOKHM algorithm.
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Danesh, M., Naghibzadeh, M., Totonchi, M.R.A., Danesh, M., Minaei, B., Shirgahi, H. (2011). Data Clustering Based on an Efficient Hybrid of K-Harmonic Means, PSO and GA. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence IV. Lecture Notes in Computer Science(), vol 6660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21884-2_2
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DOI: https://doi.org/10.1007/978-3-642-21884-2_2
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