Fuzzy Logic for Mining Episodal Association Rules in Time Series
The aim of the study reported in this paper is to use fuzzy logic to discover episodal association rules between local patterns in time series. Our method is different from other time series mining methods which mainly compare sub-series with Euclidean distance measure or its transfiguration. In order to form a sub-series, we put down values of a time series to recordsets attributes, slide a window through the attributes, and normalize them with a simple method. We cluster the normalized sub-series by fuzzy clustering to obtain its delegates which represent local episodes. We calculate rules support and confidence with membership and each sample does not arbitrarily support a single symbol so as to make the two important measures more exact and actual. We select good rules with J-measure based on membership. Stock index series are utilized to show the feasibility of the method, and empirical results show that we are able to achieve a higher SV accuracy and confidence.
data mining time series episodal association rules fuzzy logic
Bingxue Wang Yuanzhong Chen
School of Information Management & Engineering Shanghai University of Finance & Economics Shanghai 200433,China
国际会议
上海
英文
837-841
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）