Gaussian Clusters and Noise: An Approach Based on the Minimum Description Length Principle

Panu Luosto, Jyrki Kivinen, Heikki Mannila

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationDiscovery Science : 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010. Proceedings
EditorsBernhard Pfahringer, Geoff Holmes, Achim Hoffmann
Number of pages15
Place of PublicationBerlin Heidelberg
Publication date2010
ISBN (Print)978-3-642-16183-4
ISBN (Electronic)3-642-16183-9
Publication statusPublished - 2010
MoE publication typeA4 Article in conference proceedings
EventDS 2010 - Canberra, Australia
Duration: 6 Oct 20108 Oct 2010
Conference number: 13

Publication series

NameLecture Notes in Computer Science

Fields of Science

  • 113 Computer and information sciences

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