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dc.contributor.authorChen, Yi-Chengen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.contributor.authorLee, Suh-Yinen_US
dc.date.accessioned2016-03-28T00:04:09Z-
dc.date.available2016-03-28T00:04:09Z-
dc.date.issued2015-12-01en_US
dc.identifier.issn1041-4347en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2015.2454515en_US
dc.identifier.urihttp://hdl.handle.net/11536/129365-
dc.description.abstractSequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern, and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.en_US
dc.language.isoen_USen_US
dc.subjectData miningen_US
dc.subjectrepresentationen_US
dc.subjectsequential patternen_US
dc.subjecttemporal patternen_US
dc.subjectinterval-based eventen_US
dc.titleMining Temporal Patterns in Time Interval-Based Dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TKDE.2015.2454515en_US
dc.identifier.journalIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGen_US
dc.citation.volume27en_US
dc.citation.issue12en_US
dc.citation.spage3318en_US
dc.citation.epage3331en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000364853800013en_US
dc.citation.woscount0en_US
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