2005 | OriginalPaper | Buchkapitel
Clustering with Noising Method
verfasst von : Yongguo Liu, Yan Liu, Kefei Chen
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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The minimum sum of squares clustering problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this article, a recent metaheuristic technique, the noising method, is introduced to explore the proper clustering of data sets under the criterion of minimum sum of squares clustering. Meanwhile, K-means algorithm as a local improvement operation is integrated into the noising method to improve the performance of the clustering algorithm. Extensive computer simulations show that the proposed approach is feasible and effective.