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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75777

    Title: Fuzzy data mining for time-series data
    Authors: Chen, Chun-Hao;Hong, Tzung-Pei;Tseng, Vincent S.
    Contributors: 淡江大學資訊工程學系
    Keywords: Association rule;Data mining;Fuzzy set;Sliding window;Time series
    Date: 2012-01
    Issue Date: 2012-04-13 18:23:44 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.
    Relation: Applied Soft Computing 12(1), pp.536–542
    DOI: 10.1016/j.asoc.2011.08.006
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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