Approximate repeating pattern mining with gap requirements

He, D; Zhu, X; Wu, X

Approximate repeating pattern mining with gap requirements

He, D Zhu, X Wu, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2009, pp. 17 - 24
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	He, D	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Wu, X	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2009, pp. 17 - 24	en_US
dc.identifier.isbn	9781424456192	en_US
dc.identifier.issn	1082-3409	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19129
dc.description.abstract	In this paper, we define a new research problem for mining approximate repeating patterns (ARP) with gap constraints, where the appearance of a pattern is subject to an approximate matching, which is very common in biological sciences. To solve the problem, we propose an ArpGap (Approximate repeating pattern mining with Gap constraints) algorithm with three major components for approximate repeating pattern mining: (1) a data-driven pattern generation approach to avoid generating unnecessary patterns; (2) a back-tracking pattern search process to discover approximate occurrences of a pattern under gap constraints; and (3) an Apriori-like deterministic pruning approach to progressively prune patterns and cease the search process if necessary. Experimental results on synthetic and real-world protein sequences assert that ArpGap is efficient in terms of memory consumption and computational cost. © 2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI	en_US
dc.relation.isbasedon	10.1109/ICTAI.2009.8	en_US
dc.title	Approximate repeating pattern mining with gap requirements	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Newark, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we define a new research problem for mining approximate repeating patterns (ARP) with gap constraints, where the appearance of a pattern is subject to an approximate matching, which is very common in biological sciences. To solve the problem, we propose an ArpGap (Approximate repeating pattern mining with Gap constraints) algorithm with three major components for approximate repeating pattern mining: (1) a data-driven pattern generation approach to avoid generating unnecessary patterns; (2) a back-tracking pattern search process to discover approximate occurrences of a pattern under gap constraints; and (3) an Apriori-like deterministic pruning approach to progressively prune patterns and cease the search process if necessary. Experimental results on synthetic and real-world protein sequences assert that ArpGap is efficient in terms of memory consumption and computational cost. © 2009 IEEE.

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http://hdl.handle.net/10453/19129