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On mean shift-based clustering for circular data

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Abstract

Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure that had been proposed as a clustering algorithm. However, most mean shift-based clustering (MSBC) algorithms are used for numeric data. The circular data that are the directional data on the plane have been widely used in data analysis. In this paper, we propose a MSBC algorithm for circular data. Three types of mean shift implementation procedures with nonblurring, blurring and general methods are furthermore compared in which the blurring mean shift procedure is the best and recommended. The proposed MSBC for circular data is not necessary to give the number of cluster. It can automatically find a final cluster number with good clustering centers. Several numerical examples and comparisons with some existing clustering methods are used to demonstrate its effectiveness and superiority of the proposed method.

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Acknowledgments

The authors would like to thank the anonymous referees for their helpful comments in improving the presentation of this paper. This work was supported in part by the National Science Council of Taiwan, under Grant NSC-99-2118-M-033-004-MY2.

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Authors and Affiliations

  1. Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, 32023, Taiwan

    Shou-Jen Chang-Chien & Miin-Shen Yang

  2. Department of Applied Mathematics, National Hsinchu University of Education, Hsin-Chu, Taiwan

    Wen-Liang Hung

Authors
  1. Shou-Jen Chang-Chien
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  2. Wen-Liang Hung
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  3. Miin-Shen Yang
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Correspondence to Miin-Shen Yang.

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Chang-Chien, SJ., Hung, WL. & Yang, MS. On mean shift-based clustering for circular data. Soft Comput 16, 1043–1060 (2012). https://doi.org/10.1007/s00500-012-0802-z

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