An efficient approach for mining weighted frequent patterns with dynamic weights

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dc.contributor.authorDewan, Umama
dc.contributor.authorAhmed, Chowdhury Farhan
dc.contributor.authorLeung, Carson K.
dc.contributor.authorRizvee, Redwan Ahmed
dc.contributor.authorDeng, Deyu
dc.contributor.authorSouza, Joglas
dc.date.accessioned2020-03-09T20:17:23Z
dc.date.available2020-03-09T20:17:23Z
dc.date.issued2019-07
dc.date.submitted2020-03-03T00:29:02Zen_US
dc.description.abstractWeighted frequent pattern (WFP) mining is considered to be more effective than traditional frequent pattern mining because of its consideration of different semantic significance (weights) of items. However, most existing WFP algorithms assume a static weight for each item, which may not be realistically hold in many real-life applications. In this paper, we consider the concept of a dynamic weight for each item and address the situations where the weights of an item can be changed dynamically. We propose a novel tree structure called compact pattern tree for dynamic weights (CPTDW) to mine frequent patterns from dynamic weighted item containing databases. The CPTDW-tree leads to the concept of dynamic tree restructuring to produce a frequency-descending tree structure at runtime. CPTDW also ensures that no non-candidate item can appear before candidate items in any branch of the tree, and thus speeds up the construction time for prefix tree and its conditional tree during the mining process. Furthermore, as it requires only one database scan, it can be applicable to interactive, incremental, and/or stream data mining. Evaluation results show that our proposed tree structure and the mining algorithm outperforms previous methods for dynamic weighted frequent pattern mining.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.identifier.citationDewan, U., Ahmed, C.F., Leung, C.K., Rizvee, R.A., Deng, D., Souza, J.: An efficient approach for mining weighted frequent patterns with dynamic weights. In: ICDM 2019, pp. 13-27 (2019)en_US
dc.identifier.isbn978-3-942952-60-6
dc.identifier.issn1864-9734
dc.identifier.urihttp://hdl.handle.net/1993/34565
dc.language.isoengen_US
dc.publisheribai publishingen_US
dc.rightsopen accessen_US
dc.subjectData miningen_US
dc.subjectKnowledge discoveryen_US
dc.subjectWeighted frequent pattern miningen_US
dc.subjectDynamic weightsen_US
dc.titleAn efficient approach for mining weighted frequent patterns with dynamic weightsen_US
dc.typebook parten_US
local.author.affiliationFaculty of Scienceen_US
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Dewan, U., Ahmed, C.F., Leung, C.K., Rizvee, R.A., Deng, D., Souza, J.: An efficient approach for mining weighted frequent patterns with dynamic weights. In: ICDM 2019, pp. 13-27 (2019) ICDM 2019 Proceedings, "Advances in Data Mining: Applications and Theoretical Aspects", is an open access proceedings book.
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