Approximate Repeating Pattern Mining with Gap Requirements

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dc.contributor.author He, D
dc.contributor.author Zhu, X
dc.contributor.author Wu, X
dc.contributor.editor Lisa O'Conner
dc.date.accessioned 2012-10-12T03:36:16Z
dc.date.issued 2009-01
dc.identifier.citation Proc. of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI-09), 2009, pp. 17 - 24
dc.identifier.isbn 978-0-7695-3920-1
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.
dc.publisher IEEE Computer Society
dc.relation.isbasedon 10.1109/ICTAI.2009.8
dc.title Approximate Repeating Pattern Mining with Gap Requirements
dc.type Conference Proceeding
dc.parent Proc. of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI-09)
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.conference IEEE International Conference on Tools with Artificial Intelligence
dc.for 0806 Information Systems
dc.personcode 100507
dc.personcode 107283
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.date.activity 2009-11-02
dc.location.activity Newark, USA en_US
dc.description.keywords Pattern Mining, Gap Requirements, Dynamic Programming, Back-Tracking en_US
pubs.embargo.period Not known
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 - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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