HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Sinkkonen, J. Articles by Kaski, S. Search for Related Content PubMed PubMed Citation Articles by Sinkkonen, J. Articles by Kaski, S.

Clustering Based on Conditional Distributions in an Auxiliary Space

janne.sinkkonen{at}hut.fi

Samuel Kaski

samuel.kaski{at}hut.fi, Neural Networks Research Centre, Helsinki University of Technology, FIN-02015 HUT, Finland

We study the problem of learning groups or categories that are local in the continuous primary space but homogeneous by the distributions of an associated auxiliary random variable over a discrete auxiliary space. Assuming that variation in the auxiliary space is meaningful, categories will emphasize similarly meaningful aspects of the primary space. From a data set consisting of pairs of primary and auxiliary items, the categories are learned by minimizing a Kullback-Leibler divergence-based distortion between (implicitly estimated) distributions of the auxiliary data, conditioned on the primary data. Still, the categories are defined in terms of the primary space. An online algorithm resembling the traditional Hebb-type competitive learning is introduced for learning the categories. Minimizing the distortion criterion turns out to be equivalent to maximizing the mutual information between the categories and the auxiliary data. In addition, connections to density estimation and to the distributional clustering paradigm are outlined. The method is demonstrated by clustering yeast gene expression data from DNA chips, with biological knowledge about the functional classes of the genes as the auxiliary data.

This article has been cited by other articles:

				F. D. Gibbons and F. P. Roth Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation Genome Res., October 1, 2002; 12(10): 1574 - 1581. [Abstract] [Full Text] [PDF]