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. (authors' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4000 |
| Date | 09 1900 |
| Creators | Buchta, Christian, Kober, Martin, Feinerer, Ingo, Hornik, Kurt |
| Publisher | American Statistical Association |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://www.jstatsoft.org/v50/i10/paper, http://epub.wu.ac.at/4000/ |
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