Spherical k-Means Clustering

• Main Authors: Kurt Hornik, Ingo Feinerer, Martin Kober, Christian Buchta
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2012-09-01
• Series: Journal of Statistical Software
• Subjects: spherical; clustering; text mining; cosine dissimilarity; R
• Online Access: http://www.jstatsoft.org/v50/i10/paper

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.