Spherical k-means clustering

Kurt Hornik, Ingo Feinerer, Martin Kober, Christian Buchta

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

38 Downloads (Pure)

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.
OriginalspracheEnglisch
Seiten (von - bis)1 - 22
FachzeitschriftJournal of Statistical Software
Jahrgang50
Ausgabenummer10
PublikationsstatusVeröffentlicht - 1 Nov. 2012

Dieses zitieren