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Zeitschriftenartikel:

K. Hornik, I. Feinerer, M. Kober, Ch. Buchta:
"Spherical k-Means Clustering";
Journal of Statistical Software, 50 (2012), 10; S. 1 - 22.



Kurzfassung englisch:
Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational e -ciency. 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 xed-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.

Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.