Fuzzy data mining for time-series data

doi:10.1016/j.asoc.2011.08.006

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/75777

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题名:	Fuzzy data mining for time-series data
作者:	Chen, Chun-Hao;Hong, Tzung-Pei;Tseng, Vincent S.
贡献者:	淡江大學資訊工程學系
关键词:	Association rule;Data mining;Fuzzy set;Sliding window;Time series
日期:	2012-01
上传时间:	2012-04-13 18:23:44 (UTC+8)
出版者:	Amsterdam: Elsevier BV
摘要:	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.
關聯:	Applied Soft Computing 12(1), pp.536–542
DOI:	10.1016/j.asoc.2011.08.006
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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