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dc.contributor.authorFournier-Viger, Philippe
dc.contributor.authorYang, Peng
dc.contributor.authorLin, Jerry Chun-Wei
dc.contributor.authorDuong, Quang-Huy
dc.contributor.authorDam, Thu-Lan
dc.contributor.authorFrnda, Jaroslav
dc.contributor.authorŠevčík, Lukáš
dc.contributor.authorVozňák, Miroslav
dc.date.accessioned2019-07-04T07:44:25Z
dc.date.available2019-07-04T07:44:25Z
dc.date.issued2019
dc.identifier.citationAdvances in electrical and electronic engineering. 2019, vol. 17, no. 1, p. 33-44 : ill.cs
dc.identifier.issn1336-1376
dc.identifier.issn1804-3119
dc.identifier.urihttp://hdl.handle.net/10084/137584
dc.description.abstractDiscovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions. Numerous methods can identify patterns exhibiting a periodic behavior. Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly. Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxPer (maximum periodicity) threshold. As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxPer constraint. Consequently, many patterns that are almost always periodic are not presented to the user. But these patterns could be considered as interesting as they generally appear periodically. To address this issue, this paper suggests to use three measures to identify periodic patterns. These measures are named average, maximum and minimum periodicity, respectively. They are each designed to evaluate a different aspect of the periodic behavior of patterns. By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements. The designed algorithm has been evaluated on several datasets. Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database.cs
dc.languageNeuvedenocs
dc.language.isoencs
dc.publisherVysoká škola báňská - Technická univerzita Ostravacs
dc.relation.ispartofseriesAdvances in electrical and electronic engineeringcs
dc.relation.urihttp://dx.doi.org/10.15598/aeee.v17i1.3185
dc.rights© Vysoká škola báňská - Technická univerzita Ostrava
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectitemset miningcs
dc.subjectperiodic patterncs
dc.subjectperiodicitycs
dc.subjectaverage periodicitycs
dc.titleDiscovering Periodic Itemsets Using Novel Periodicity Measurescs
dc.typearticlecs
dc.identifier.doi10.15598/aeee.v17i1.3185
dc.rights.accessopenAccess
dc.type.versionpublishedVersion
dc.type.statusPeer-reviewed


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