Multiplicative mixture models for overlapping clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The problem of overlapping clustering, where a point is allowed to belong to multiple clusters, is becoming increasingly important in a variety of applications. In this paper, we present an overlapping clustering algorithm based on multiplicative mixture models. We analyze a general setting where each component of the multiplicative mixture is from an exponential family, and present an efficient alternating maximization algorithm to learn the model and infer overlapping clusters. We also show that when each component is assumed to be a Gaussian, we can apply the kernel trick leading to non-linear cluster separators and obtain better clustering quality. The efficacy of the proposed algorithms is demonstrated using experiments on both UCI enchmark datasets and a microarray gene expression dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Number of pages6
StatePublished - 2008
Externally publishedYes
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other8th IEEE International Conference on Data Mining, ICDM 2008

ASJC Scopus subject areas

  • General Engineering


Dive into the research topics of 'Multiplicative mixture models for overlapping clustering'. Together they form a unique fingerprint.

Cite this