Recent Experiences in Parameter-Free Data Mining

Authors: Kimihito Ito, Thomas Zeugmann¹, and Yu Zhu

Source: Computer and Information Science, Proceedings of the 25th International Symposium on Computer and Information Sciences.
(Erol Gelenbe, Ricardo Lent, Georgia Sakellari, Ahmet Sacan and Hakki Toroslu and Adnan Yazici, Eds.)
Lecture Notes in Electrical Engineering 62, pp. 365-371, Springer 2010.

Abstract. Recent results supporting the usefulness of the normalized compression distance for the task to classify genome sequences of virus data are reported. Specifically, the problem to cluster the hemagglutinin (HA) sequences of influenza virus data for the HA gene in dependence on the host and subtype of the virus, and the classification of dengue virus genome data with respect to their four serotypes are studied.

A comparison is made with respect to hierarchical and spectral clustering via the kLine algorithm by Fischer and Poland (2004), respectively, and with respect to the standard compressors bzlip, ppmd, and zlib.

Our results are very promising and show that one can obtain an (almost) perfect clustering for all the problems studied.