Sart: A New Association Rule Method For Mining Sequential Patterns In Time Series Of Climate Data

dc.creatorCano M.D.
dc.creatorSantos M.T.P.
dc.creatorDe Avila A.M.H.
dc.creatorRomani L.A.S.
dc.creatorTraina A.J.M.
dc.creatorRibeiro M.X.
dc.date2012
dc.date2015-06-26T20:29:36Z
dc.date2015-11-26T14:26:02Z
dc.date2015-06-26T20:29:36Z
dc.date2015-11-26T14:26:02Z
dc.date.accessioned2018-03-28T21:28:53Z
dc.date.available2018-03-28T21:28:53Z
dc.identifier9783642311369
dc.identifierLecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 7335 LNCS, n. PART 3, p. 743 - 757, 2012.
dc.identifier3029743
dc.identifier10.1007/978-3-642-31137-6_56
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84863904204&partnerID=40&md5=74d85ea28516f847253a7071eb83698e
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/97092
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/97092
dc.identifier2-s2.0-84863904204
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1245947
dc.descriptionTechnological advancement has enabled improvements in the technology of sensors and satellites used to gather climate data. The time series mining is an important tool to analyze the huge quantity of climate data. Here, we propose the Sequential Association Rules from Time series - SART method to mine association rules in time series that keeps the information of time between related events through an overlapped sliding-window approach. Also the proposed method mines association rules, while the previous ones produce frequent sequences, adding the semantic information of confidence, which was not previously defined by sequential patterns. Experiments were conducted with real data collected from climate sensors. The results showed that the proposed method increases the number of mined patterns when compared with the traditional sequential mining, revealing related events that occur over time. Also, the method adds the semantic information related to the confidence and time to the mined patterns. © 2012 Springer-Verlag.
dc.description7335 LNCS
dc.descriptionPART 3
dc.description743
dc.description757
dc.descriptionUniversidade Federal da Bahia (UFBA),Universidade Federal do Reconcavo da Bahia (UFRB),Universidade Estadual de Feira de Santana (UEFS),University of Perugia,University of Basilicata (UB)
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dc.languageen
dc.publisher
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsfechado
dc.sourceScopus
dc.titleSart: A New Association Rule Method For Mining Sequential Patterns In Time Series Of Climate Data
dc.typeActas de congresos


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