Publications in Scientific Journals:
K. Hornik, I. Feinerer, M. Kober, Ch. Buchta:
"Spherical k-Means Clustering";
Journal of Statistical Software,
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
Created from the Publication Database of the Vienna University of Technology.