2010 DiscoveringSignificantRelaxedOr

• (Fang et al., 2010) ⇒ Qiong Fang, Wilfred Ng, and Jianlin Feng. (2010). “Discovering Significant Relaxed Order-preserving Submatrices.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835861

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• http://scholar.google.com/scholar?q=%22Discovering+significant+relaxed+order-preserving+submatrices%22+2010
• http://portal.acm.org/citation.cfm?id=1835861&preflayout=flat#citedby

Quotes

Abstract

Mining order-preserving submatrix (OPSM) patterns has received much attention from researchers, since in many scientific applications, such as those involving gene expression data, it is natural to express the data in a matrix and also important to find the order-preserving submatrix patterns. However, most current work assumes the noise-free OPSM model and thus is not practical in many real situations when sample contamination exists.

In this paper, we propose a relaxed OPSM model called ROPSM. The ROPSM model supports mining more reasonable noise-corrupted OPSM patterns than another well-known model called AOPC (approximate order-preserving cluster). While OPSM mining is known to be an NP-hard problem, mining ROPSM patterns is even a harder problem. We propose a novel method called ROPSM-Growth to mine ROPSM patterns. Specifically, two pattern growing strategies, such as column-centric strategy and row-centric strategy, are presented, which are effective to grow the seed OPSMs into significant ROPSMs. An effective median-rank based method is also developed to discover the underlying true order of conditions involved in an ROPSM pattern. experiments on a biological dataset show that the ROPSM model better captures the characteristics of noise in gene expression data matrix compared to the AOPC model. Importantly, we find that our approach is able to detect more quality biologically significant patterns with comparable efficiency with the counterparts of AOPC. Specifically, at least 26.6% (75 out of 282) of the patterns mined by our approach are strongly associated with more than 10 gene categories (high biological significance), which is 3 times better than that obtained from using the AOPC approach.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 DiscoveringSignificantRelaxedOr	Qiong Fang Wilfred Ng Jianlin Feng			Discovering Significant Relaxed Order-preserving Submatrices		KDD-2010 Proceedings		10.1145/1835804.1835861		2010