An Iterative Non-parametric Clustering Algorithm Based on Local Shrinking

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Date

2006

Authors

Wang, Xiaogang
Qiu, Weiliang
Zamar, Ruben H.

Journal Title

Journal ISSN

Volume Title

Publisher

Computational Statistics and Data Analysis

Abstract

In 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.

Description

Keywords

Automatic clustering, K-nearest neighbors, Local shrinking, Number of clusters, Strength of clusters

Citation

X. 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