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Recursive partitioning clustering tree algorithm

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Abstract

Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if–then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.

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Acknowledgments

The authors would like to thank the editor and the reviewers for their useful comments and suggestions, which were greatly helpful in improving the quality of the paper. This research was supported by Brain Korea PLUS, Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (2013007724), and the Ministry of Knowledge Economy in Korea under the IT R&D Infrastructure Program supervised by the National IT Industry Promotion Agency (NIPA) [NIPA-2011-(B1110-1101-0002)].

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Authors and Affiliations

  1. School of Industrial Management Engineering, Korea University, Anam Seongbuk, Seoul, 136-713, Republic of Korea

    Ji Hoon Kang, Chan Hee Park & Seoung Bum Kim

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  1. Ji Hoon Kang
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  2. Chan Hee Park
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  3. Seoung Bum Kim
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Correspondence to Seoung Bum Kim.

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Kang, J.H., Park, C.H. & Kim, S.B. Recursive partitioning clustering tree algorithm. Pattern Anal Applic 19, 355–367 (2016). https://doi.org/10.1007/s10044-014-0399-1

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