Sliding window based weighted periodic pattern mining over time series data

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dc.contributor.authorRizvee, Redwan Ahmed
dc.contributor.authorShahin, Md Shahadat Hossain
dc.contributor.authorAhmed, Chowdhury Farhan
dc.contributor.authorLeung, Carson K.
dc.contributor.authorDeng, Deyu
dc.contributor.authorMai, Jiaxing Jason
dc.date.accessioned2020-03-09T20:19:33Z
dc.date.available2020-03-09T20:19:33Z
dc.date.issued2019-07
dc.date.submitted2020-03-03T00:31:18Zen_US
dc.description.abstractSliding windows have been crucial in mining time series. Many existing studies focus on reconstruction of the underlying structure (e.g., suffix tree) for each new window. However, when the window size is large or when the window slides frequently, reconstruction may perform poorly. In this paper, we propose a solution that dynamically updates the structure (rather than reconstruction for each modification or sliding). Moreover, many existing studies rely on the weight of maximum weighted item in the database to avoid testing unnecessary patterns when mining weighted periodic patterns from time series, but it may still require lots of weight checking to determine whether a pattern is a candidate. In this paper, we also propose an additional solution to address this problem by discarding unimportant patterns beforehand so as to speed up the candidate generation process. Evaluation results on real-life datasets show the effectiveness of our two solutions in handling sliding window and pruning redundant candidate patterns.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.identifier.citationRizvee, R.A., Shahin, M.S.H., Ahmed, C.F., Leung, C.K., Deng, D., Mai, J.J.: Sliding window based weighted periodic pattern mining over time series data. In: ICDM 2019, pp. 118-132 (2019)en_US
dc.identifier.isbn978-3-942952-60-6
dc.identifier.issn1864-9734
dc.identifier.urihttp://hdl.handle.net/1993/34566
dc.language.isoengen_US
dc.publisheribai publishingen_US
dc.rightsopen accessen_US
dc.subjectTime seriesen_US
dc.subjectWeighted periodic pattern miningen_US
dc.subjectDynamic databaseen_US
dc.subjectSliding windowen_US
dc.subjectPruningen_US
dc.titleSliding window based weighted periodic pattern mining over time series dataen_US
dc.typebook parten_US
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Rizvee, R.A., Shahin, M.S.H., Ahmed, C.F., Leung, C.K., Deng, D., Mai, J.J.: Sliding window based weighted periodic pattern mining over time series data. In: ICDM 2019, pp. 118-132 (2019) ICDM 2019 Proceedings, "Advances in Data Mining: Applications and Theoretical Aspects", is an open access proceedings book.
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