Mining sequence pattern from time series based on inter-relevant successive trees model

Authors:
Haiquan Zeng;Zhan Shen;Yunfa Hu
Affiliations:
Computer Science Dept., Fudan University, Shanghai, P.R.China;Computer Science Dept., Fudan University, Shanghai, P.R.China;Computer Science Dept., Fudan University, Shanghai, P.R.China
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Segment-based approach for subsequence searches in sequence databases

Proceedings of the 2001 ACM symposium on Applied computing

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Abstract

In this paper, a novel method is proposed to discover frequent pattern from time series. It first segments time series based on perceptually important points, then converted it into meaningful symbol sequences by the relative scope, finally used a new mining model to find frequent patterns. Compared with the previous methods, the method is simpler and more efficient.