An Iterative Non-parametric Clustering Algorithm Based on Local Shrinking

dc.contributor.authorWang, Xiaogang
dc.contributor.authorQiu, Weiliang
dc.contributor.authorZamar, Ruben H.
dc.date.accessioned2007-03-27T18:52:27Z
dc.date.available2007-03-27T18:52:27Z
dc.date.issued2006
dc.description.abstractIn this paper, we propose a new non-parametric clustering method based on local shrinking. 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 K is decided by optimizing index functions that measure the strengths of clusters. The number of clusters and the final partition are determined automatically without any input parameter except the stopping rule for convergence. Our performance studies have shown that this algorithm converges fast and achieves high accuracy.
dc.identifier.citationX. Wang, W. Qiu, and R. H. Zamar, “CLUES: A non-parametric clustering method based on local shrinking,” Computational Statistics & Data Analysis, vol. 52, no. 1, pp. 286–298, Sep. 2007. doi:10.1016/j.csda.2006.12.016
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/10315/925
dc.identifier.urihttps://doi.org/10.1016/j.csda.2006.12.016
dc.language.isoen
dc.publisherComputational Statistics and Data Analysis
dc.rights© 2006. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectAutomatic clustering
dc.subjectK-nearest neighbors
dc.subjectLocal shrinking
dc.subjectNumber of clusters
dc.subjectStrength of clusters
dc.titleAn Iterative Non-parametric Clustering Algorithm Based on Local Shrinking
dc.typeArticle

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