Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity

Authors:
Tina Geweniger;Frank-Michael Schleif;Alexander Hasenfuss;Barbara Hammer;Thomas Villmann
Affiliations:
Dept. of Medicine, University Leipzig, Leipzig, Germany 04103;Dept. of Medicine, University Leipzig, Leipzig, Germany 04103;Clausthal Univ. of Tech., Inst. of CS, Clausthal, Germany 38678;Clausthal Univ. of Tech., Inst. of CS, Clausthal, Germany 38678;Dept. of Medicine, University Leipzig, Leipzig, Germany 04103
Venue:
Advances in Neuro-Information Processing
Year:
2009

Citing 0
Cited 2

Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means

WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Median fuzzy c-means for clustering dissimilarity data

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a comparison of multiple cluster algorithms and their suitability for clustering text data. The clustering is based on similarities only, employing the Kolmogorov complexity as a similiarity measure. This motivates the set of considered clustering algorithms which take into account the similarity between objects exclusively. Compared cluster algorithms are Median kMeans, Median Neural Gas, Relational Neural Gas, Spectral Clustering and Affinity Propagation.