Spherical k-Means Clustering

Kurt Hornik, Ingo Feinerer, Martin Kober and Christian Buchta

Journal of Statistical Software, 2012, vol. 050, issue i10

Abstract: 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.

Date: 2012-09-18
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:050:i10

DOI: 10.18637/jss.v050.i10

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