N-Dimensional Association Rules in Time Series Databases

摘要：

In this paper,A general methodology for knowledge discovery in TSDB is introduced. It includes cleaning and filtering of the original series data,identifying the most important predicting attributes,and extracting a set of association rules that can be used to predict the time series behavior in the future. To acquire segments,We treat attributes of database by sliding a window of width W through recordsets in the database. Signal processing techniques and the information-theoretic fuzzy approach are used to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation.

关键词： Data mining fuzzy association rules attributes of database time series databases

作者: Wang Bingxue

作者单位: School of Information Management and Engineering,Shanghai University of Finance and Economics,Shanghai,200433,China

会议类型: 国际会议

会议名称: The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)

会议地点: 上海

会议语种:英文

页码: 468-474

在线出版日期: 2007-06-04（万方平台首次上网日期，不代表论文的发表时间）