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dc.contributor.authorDuong, Quang-Huy
dc.contributor.authorRamampiaro, Heri
dc.contributor.authorNørvåg, Kjetil
dc.contributor.authorFournier-Viger, Philippe
dc.contributor.authorDam, Thu-Lan
dc.date.accessioned2019-02-18T13:20:56Z
dc.date.available2019-02-18T13:20:56Z
dc.date.created2018-05-24T23:05:43Z
dc.date.issued2018
dc.identifier.citationKnowledge-Based Systems. 2018, 157 34-51.nb_NO
dc.identifier.issn0950-7051
dc.identifier.urihttp://hdl.handle.net/11250/2585990
dc.description.abstractThis paper presents an efficient algorithm for detecting changes (drifts) in the utility distributions of patterns, named High Utility Drift Detection in Transactional Data Stream (HUDD-TDS). The algorithm is specifically suitable for quantitative data streams, where each item has a unit profit, and non-binary purchase quantities are allowed. We propose a method that enables the HUDD-TDS algorithm to be used in an online setting to detect drifts. An important property of HUDD-TDS is that it can quickly adapt to changes in streams, while considering older transactions to be less important than new ones. Furthermore, the proposed method applies statistical testing based on Hoeffding bound with Bonferroni correction in order to ensure that only significant changes are reported to the user. This test allows identifying a change (drift) if the difference between current and the previous time window is significant in terms of utility distribution. In this work, we focus on both local and global utility drifts. A local utility drift is a drift in the utility distribution of a single pattern, whereas a global utility drift is a change in the utilities of all high utility itemsets. In order to be able to compute the similarity of different high utility itemsets to detect drifts, we propose a new distance measure function. The results of our experiments on both real world and synthetic datasets show the feasibility and efficiency of the proposed HUDD-TDS algorithm.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.relation.urihttp://www.idi.ntnu.no/~heri/papers/DuongKBS2018.pdf
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectDatagruvedriftnb_NO
dc.subjectDataminingnb_NO
dc.titleHigh Utility Drift Detection in Quantitative Data Streamsnb_NO
dc.title.alternativeHigh Utility Drift Detection in Quantitative Data Streamsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.subject.nsiVDP::Datateknologi: 551nb_NO
dc.subject.nsiVDP::Computer technology: 551nb_NO
dc.source.pagenumber34-51nb_NO
dc.source.volume157nb_NO
dc.source.journalKnowledge-Based Systemsnb_NO
dc.identifier.doi10.1016/j.knosys.2018.05.014
dc.identifier.cristin1586601
dc.relation.projectAndre: 548172nb_NO
dc.description.localcode© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 22.5.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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