[test by reto]
|Authors:||Kurt Hornik, Ingo Feinerer, Martin Kober, Christian Buchta|
|Title:||Spherical k-Means Clustering|
|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.
Page views:: 12444. Submitted: 2010-11-19. Published: 2012-09-18.
Spherical k-Means Clustering
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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.