Quotation Hornik, Kurt, Feinerer, Ingo, Kober, Martin, Buchta, Christian. 2012. Spherical k-means clustering. Journal of Statistical Software 50 (10): 1-22.




Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical k-means clustering is one approach to address both issues, employing cosine dissimilarities to perform prototype-based partitioning of term weight representations of the documents. This paper presents the theory underlying the standard spherical k-means problem and suitable extensions, and introduces the R extension package skmeans which provides a computational environment for spherical k-means clustering featuring several solvers: a fixed-point and genetic algorithm, and interfaces to two external solvers (CLUTO and Gmeans). Performance of these solvers is investigated by means of a large scale benchmark experiment.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Statistical Software
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title Spherical k-means clustering
Volume 50
Number 10
Year 2012
Page from 1
Page to 22
URL http://www.jstatsoft.org/v50/i10/


Hornik, Kurt (Details)
Kober, Martin (Former researcher)
Buchta, Christian
Feinerer, Ingo
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
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