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On hierarchical clustering in sufficient dimension reduction

Title
On hierarchical clustering in sufficient dimension reduction
Authors
Yoo C.Yoo Y.Um H.Y.Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2020
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843JCR Link
Citation
Communications for Statistical Applications and Methods vol. 27, no. 4, pp. 431 - 443
Keywords
Central subspaceHierarchical clusteringInformative predictor subspaceK-means clusteringMultivariate slicingSufficient dimension reduction
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI scopus
Document Type
Article
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. © 2020 The Korean Statistical Society, and Korean International Statistical Society.
DOI
10.29220/CSAM.2020.27.4.431
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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