2013 FlexibleandRobustCoRegularizedM

• (Cheng et al., 2013) ⇒ Wei Cheng, Xiang Zhang, Zhishan Guo, Yubao Wu, Patrick F. Sullivan, and Wei Wang. (2013). “Flexible and Robust Co-regularized Multi-domain Graph Clustering.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487582

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Flexible+and+Robust+Co-regularized+Multi-domain+Graph+Clustering
• http://dl.acm.org/citation.cfm?id=2487575.2487582&preflayout=flat#citedby

Quotes

Author Keywords

• Co-regularization; data mining; graph clustering; nonnegative matrix factorization

Abstract

Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Despite the previous success, existing multi-view graph clustering methods usually assume that different views are available for the same set of instances. Thus instances in different domains can be treated as having strict one-to-one relationship. In many real-life applications, however, data instances in one domain may correspond to multiple instances in another domain. Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge. In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization(NMF), to tackle these challenges. CGC has several advantages over the existing methods. First, it supports many-to-many cross-domain instance relationship. Second, it incorporates weight on cross-domain relationship. Third, it allows partial cross-domain mapping so that graphs in different domains may have different sizes. Finally, it provides users with the extent to which the cross-domain instance relationship violates the in-domain clustering structure, and thus enables users to re-evaluate the consistency of the relationship. Extensive experimental results on UCI benchmark data sets, newsgroup data sets and biological interaction networks demonstrate the effectiveness of our approach.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 FlexibleandRobustCoRegularizedM	Wei Wang Xiang Zhang Wei Cheng Zhishan Guo Yubao Wu Patrick F. Sullivan			Flexible and Robust Co-regularized Multi-domain Graph Clustering				10.1145/2487575.2487582		2013