Authors: Hansen, Pierre
Jaumard, Brigitte
Mladenović, Nenad 
Title: Minimum sum of squares clustering in a low dimensional space
Journal: Journal of Classification
Volume: 15
Issue: 1
First page: 37
Last page: 55
Issue Date: 1-Jan-1998
Rank: M21
ISSN: 0176-4268
DOI: 10.1007/s003579900019
Abstract: 
Clustering with a criterion which minimizes the sum of squared distances to cluster centroids is usually done in a heuristic way. An exact polynomial algorithm, with a complexity in O(N p+1 logN), is proposed for minimum sum of squares hierarchical divisive clustering of points in a p-dimensional space with small p. Empirical complexity is one order of magnitude lower. Data sets with N = 20000 for p = 2, N = 1000 for p = 3, and N = 200 for p = 4 are clustered in a reasonable computing time.
Publisher: Springer Link

Show full item record

SCOPUSTM   
Citations

39
checked on May 23, 2024

Page view(s)

54
checked on May 9, 2024

Google ScholarTM

Check

Altmetric

Altmetric


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