Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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On hierarchical clustering in sufficient dimension reduction

  • Yoo, Chaeyeon (Department of Statistics, Ewha Womans University) ;
  • Yoo, Younju (Department of Statistics, Ewha Womans University) ;
  • Um, Hye Yeon (Department of Statistics, Ewha Womans University) ;
  • Yoo, Jae Keun (Department of Statistics, Ewha Womans University)
  • Received : 2020.03.16
  • Accepted : 2020.04.17
  • Published : 2020.07.31
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Abstract

The K-means clustering algorithm has had successful application in sufficient dimension reduction. Unfortunately, the algorithm does have reproducibility and nestness, which will be discussed in this paper. These are clear deficits for the K-means clustering algorithm; however, the hierarchical clustering algorithm has both reproducibility and nestness, but intensive comparison between K-means and hierarchical clustering algorithm has not yet been done in a sufficient dimension reduction context. In this paper, we rigorously study the two clustering algorithms for two popular sufficient dimension reduction methodology of inverse mean and clustering mean methods throughout intensive numerical studies. Simulation studies and two real data examples confirm that the use of hierarchical clustering algorithm has a potential advantage over the K-means algorithm.

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Acknowledgement

For Chaeyeon Yoo, this work was supported by the BK21 Plus Project through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (22A20130011003). For Jae Keun Yoo, this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (NRF-2019R1F1A1050715/2019R1A6A1A11051177).

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