An Efficient Approach for Mining Segment-Wise Intervention Rules in Time-Series Streams

摘要：

Huge time-series stream data are collected every day from many areas, and their trends may be impacted by outside events, hence biased from its normal behavior. This phenomenon is referred as inter vention. Intervention rule mining is a new research direction in data min ing with great challenges. To solve these challenges, this study makes the following contributions: (a) Proposes a framework to detect intervention events in time-series streams, (b) Proposes approaches to evaluate the impact of intervention events, and (c) Conducts extensive experiments both on real data and on synthetic data. The results of the experiments show that the newly proposed methods reveal interesting knowledge and perform well with good accuracy and efficiency.

关键词： Time-series stream Intervention events Data Mining

作者: Yue Wang Jie Zuo Ning Yang Lei Duan Hong-Jun Li Jun Zhu

作者单位: DB&KE Lab., School of Computer Science, Siehuan University, Chengdu, 610065, China China Birth Defect Monitoring Centre, Sichuan University, Chengdu, 610065, China

会议类型: 国际会议

会议名称: 11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)

会议地点: 九寨沟

会议语种:英文

页码: 238-249

在线出版日期: 2010-07-14（万方平台首次上网日期，不代表论文的发表时间）