CLUES: A non-parametric clustering method based on local shrinking

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CLUES: A non-parametric clustering method based on local shrinking

TitleCLUES: A non-parametric clustering method based on local shrinking
Publication TypeJournal Article
Year of Publication2007
AuthorsWang, X, Qiu, W, Zamar, RH
JournalCOMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume52
Pagination286-298
Date PublishedSEP 15
Type of ArticleArticle
ISSN0167-9473
Keywordsautomatic clustering, K-nearest neighbors, local shrinking, number of clusters
AbstractA novel non-parametric clustering method based on non-parametric local shrinking is proposed. Each data point is transformed in such a way that it moves a specific distance toward a cluster center. The direction and the associated size of each movement are determined by the median of its K-nearest neighbors. This process is repeated until a pre-defined convergence criterion is satisfied. The optimal value of the number of neighbors is determined by optimizing some commonly used index functions that measure the strengths of clusters generated by the algorithm. The number of clusters and the final partition are determined automatically without any input parameter except the stopping rule for convergence. Experiments on simulated and real data sets suggest that the proposed algorithm achieves relatively high accuracies when compared with classical clustering algorithms. (c) 2007 Elsevier B.V. All rights reserved.
DOI10.1016/j.csda.2006.12.016