2008 PermuPatternDiscoveryofMutableP

• (Hu et al., 2008) ⇒ Meng Hu, Jiong Yang, and Wei Su. (2008). “Permu-pattern: Discovery of Mutable Permutation Patterns with Proximity Constraint.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401932

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Cited By

• http://scholar.google.com/scholar?q=%22Permu-pattern%3A+discovery+of+mutable+permutation+patterns+with+proximity+constraint%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401932&preflayout=flat#citedby

Quotes

Abstract

Pattern discovery in sequences is an important problem in many applications, especially in computational biology and text mining. However, due to the noisy nature of data, the traditional sequential pattern model may fail to reflect the underlying characteristics of sequence data in these applications. There are two challenges: First, the mutation noise exists in the data, and therefore symbols may be misrepresented by other symbols; Secondly, the order of symbols in sequences could be permutated. To address the above problems, in this paper we propose a new sequential pattern model called mutable permutation patterns. Since the Apriori property does not hold for our permutation pattern model, a novel Permu-pattern algorithm is devised to mine frequent mutable permutation patterns from sequence databases. A reachability property is identified to prune the candidate set. Last but not least, we apply the permutation pattern model to a real genome dataset to discover gene clusters, which shows the effectiveness of the model. A large amount of synthetic data is also utilized to demonstrate the efficiency of the Permu-pattern algorithm.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 PermuPatternDiscoveryofMutableP	Meng Hu Jiong Yang Wei Su			Permu-pattern: Discovery of Mutable Permutation Patterns with Proximity Constraint				10.1145/1401890.1401932