View : 19 Download: 0

D-optimality criterion for weighting variables in K-means clustering

Title
D-optimality criterion for weighting variables in K-means clustering
Authors
Lim Y.B.Park Y.J.Huh M.-H.
Ewha Authors
임용빈
SCOPUS Author ID
임용빈scopus
Issue Date
2009
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
vol. 38, no. 4, pp. 391 - 396
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
The aim of the study is how to achieve best K-means clustering structure so that k groups uncovered reveal more meaningful within-group coherence by assigning weights w 1, ..., w m to m clustering variables Z 1, ..., Z m. We propose Wilks' lambda as a criterion to be minimized with respect to variable weights w 1, ..., w m. This criterion, that is the ratio of the determinant of the within-cluster sums of squares and cross products matrix and that of the between clusters sums of squares and cross products matrix, is equivalent to the D-optimality criterion in the optimal design theory and related to minimization of the volume of the simultaneous confidence region of the cluster means. We will present the computing algorithm for such K-means clustering and numerical examples, among which one is simulated, two are real and the other one is the real data set augmented with additional simulated noise variables. © 2009 The Korean Statistical Society.
DOI
10.1016/j.jkss.2009.04.003
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE