PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian Clusters and Noise: An Approach Based on the Minimum Description Length Principle
Panu Luosto, Jyrki Kivinen and Heikki Mannila
In: 13th International Conference on Discovery Science, 6-8 Oct 2010, Canberra, Australia.

Abstract

We introduce a well-grounded minimum description length (MDL) based quality measure for a clustering consisting of either spherical or axis-aligned normally distributed clusters and a cluster with a uniform distribution in an axis-aligned rectangular box. The uniform component extends the practical usability of the model e.g. in the presence of noise, and using the MDL principle for the model selection makes comparing the quality of clusterings with a different number of clusters possible. We also introduce a novel search heuristic for finding the best clustering with an unknown number of clusters. The heuristic is based on the idea of moving points from the Gaussian clusters to the uniform one and using MDL for determining the optimal amount of noise. Tests with synthetic data having a clear cluster structure imply that the search method is effective in finding the intuitively correct clustering.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7988
Deposited By:Panu Luosto
Deposited On:17 March 2011