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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1724

Title: A Weighted Sum Validity Function for Clustering With a Hybrid Niching Genetic Algorithm
Authors: Sheng, W
Swift, S
Zhang, L
Liu, X
Keywords: Cluster validity
Clustering
Evolutionary computation
Publication Date: 2005
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man and Cybernetics - Part B 35 (6): 1156-1167, Dec 2005
Abstract: Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.
URI: http://bura.brunel.ac.uk/handle/2438/1724
DOI: http://dx.doi.org/10.1109/TSMCB.2005.850173
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers

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