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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorLin, Jerry Chun-Wei
dc.contributor.authorNørvåg, Kjetil
dc.contributor.authorRamampiaro, Heri
dc.contributor.authorYu, Philip S.
dc.date.accessioned2021-06-07T07:26:06Z
dc.date.available2021-06-07T07:26:06Z
dc.date.created2021-03-06T14:30:29Z
dc.date.issued2021
dc.identifier.citationACM Transactions on Management Information Systems (TMIS). 2021, 12 (2), 1-36.en_US
dc.identifier.issn2158-656X
dc.identifier.urihttps://hdl.handle.net/11250/2758069
dc.description.abstractThis article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.en_US
dc.language.isoengen_US
dc.publisherACMen_US
dc.titleExploring Decomposition for Solving Pattern Mining Problemsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber1-36en_US
dc.source.volume12en_US
dc.source.journalACM Transactions on Management Information Systems (TMIS)en_US
dc.source.issue2en_US
dc.identifier.doihttps://doi.org/10.1145/3439771
dc.identifier.cristin1896052
dc.description.localcode© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, https://doi.org/10.1145/3340531.3412097en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextpostprint
cristin.qualitycode1


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