Abstract
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 intervention. Intervention rule mining is a new research direction in data mining 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.
Supported by the National Science Foundation of China under Grant No.60773169, the 11th Five Years Key Programs for Sci.and Tech. Development of China under grant No.2006BAI05A01.
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Wang, Y., Zuo, J., Yang, N., Duan, L., Li, HJ., Zhu, J. (2010). An Efficient Approach for Mining Segment-Wise Intervention Rules in Time-Series Streams. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds) Web-Age Information Management. WAIM 2010. Lecture Notes in Computer Science, vol 6184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14246-8_25
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DOI: https://doi.org/10.1007/978-3-642-14246-8_25
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