02039nam a2200253 a 450000100080000000500110000800800410001910000160006024501260007626001440020230000160034649000470036250001590040952010240056865000250159265300160161765300250163365300220165870000210168070000230170170000210172470000210174570000190176619447132020-01-08       2012    bl uuuu     u00u1 u    #d1 aCANO, M. D.  aSARTba new association rule method for mining sequential patterns in time series of climate data.h[electronic resource]  aIn: INTERNATIONAL CONFERENCE, 12., 2012, Salvador de Bahia. Computational science and its applications: proceedings. Berlin: Springerc2012  ap. 743-757.  a(Lecture notes in computer science, 7335).  aPart III. ICCSA 2012. Editores: Beniamino Murgante, Osvaldo Gervasi, Sanjay Misra, Nadia, Nejah, Ana Maria A. C. Rocha, David Taniar, Bernardy I, Apduhan.  aAbstract. Technological 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.  aTime series analysis  aData mining  aMineração de dados  aSéries temporais1 aSANTOS, M. T. P.1 aAVILA, A. M. H. de1 aROMANI, L. A. S.1 aTRAINA, A. J. M.1 aRIBEIRO, M. X.