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

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.

• Type: Conference
• Year: 2008
• Where: ICONIP
• Authors: Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann
• Keywords: Cluster Algorithms, ICONIP 2008, Information Technology, Multiple Cluster Algorithms, Neural Gas