Approximate Repeating Pattern Mining with Gap Requirements

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dc.contributor.author He, Dan en_US
dc.contributor.author Zhu, Xingquan en_US
dc.contributor.author Wu, Xindong en_US
dc.contributor.editor Lisa O'Conner en_US
dc.date.accessioned 2012-10-12T03:36:16Z
dc.date.available 2012-10-12T03:36:16Z
dc.date.issued 2009 en_US
dc.identifier 2009001670 en_US
dc.identifier.citation He Dan, Zhu Xingquan, and Wu Xindong 2009, 'Approximate Repeating Pattern Mining with Gap Requirements', , IEEE Computer Society, Washington DC, USA, , pp. 17-24. en_US
dc.identifier.issn 978-0-7695-3920-1 en_US
dc.identifier.other E1 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. en_US
dc.language English en_US
dc.publisher IEEE Computer Society en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/ICTAI.2009.8 en_US
dc.title Approximate Repeating Pattern Mining with Gap Requirements en_US
dc.parent Proc. of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI-09) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Washington DC, USA en_US
dc.identifier.startpage 17 en_US
dc.identifier.endpage 24 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080600 en_US
dc.personcode 0000059200 en_US
dc.personcode 107283 en_US
dc.personcode 100507 en_US
dc.percentage 100 en_US
dc.classification.name Information Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Conference on Tools with Artificial Intelligence en_US
dc.date.activity 20091102 en_US
dc.location.activity Newark, USA en_US
dc.description.keywords Pattern Mining, Gap Requirements, Dynamic Programming, Back-Tracking en_US
dc.staffid 100507 en_US


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