2012 MultiViewClusteringUsingMixture

• (Günnemann et al., 2012) ⇒ Stephan Günnemann, Ines Färber, and Thomas Seidl. (2012). “Multi-view Clustering Using Mixture Models in Subspace Projections.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339553

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Multi-view+Clustering+Using+Mixture+Models+in+Subspace+Projections
• http://dl.acm.org/citation.cfm?id=2339530.2339553&preflayout=flat#citedby

Quotes

Author Keywords

• Clustering; data mining; generative model; graphical model; model-based clustering

Abstract

Detecting multiple clustering solutions is an emerging research field. While data is often multi-faceted in its very nature, traditional clustering methods are restricted to find just a single grouping. To overcome this limitation, methods aiming at the detection of alternative and multiple clustering solutions have been proposed. In this work, we present a Bayesian framework to tackle the problem of multi-view clustering. We provide multiple generalizations of the data by using multiple mixture models. Each mixture describes a specific view on the data by using a mixture of Beta distributions in subspace projections. Since a mixture summarizes the clusters located in similar subspace projections, each view highlights specific aspects of the data. In addition, our model handles overlapping views, where the mixture components compete against each other in the data generation process. For efficiently learning the distributions, we propose the algorithm MVGen that exploits the ICM principle and uses Bayesian model selection to trade-off the cluster model's complexity against its goodness of fit. With experiments on various real-world data sets, we demonstrate the high potential of MVGen to detect multiple, overlapping clustering views in subspace projections of the data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MultiViewClusteringUsingMixture	Thomas Seidl Stephan Günnemann Ines Färber			Multi-view Clustering Using Mixture Models in Subspace Projections				10.1145/2339530.2339553		2012