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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
H. Mannila, H. Toivonen. Discovering generalized episodes using minimal occurrences, In proceedings of KDD’96. Portland Oregon: AAAI Press 146–151.
G. Das, K. Lin Rule Discovery from Time Series. In proceedings of KDD’98 1998, 34–43.
F. Hoppner. Learning Temporal Rules from State Sequences. IJCAI Workshop on Learning from Temporal and Spatial Data 2001. 145–153.
J. E. Keogh, P. Smyth: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. In proceedings of KDD’97 1997: 24–30.
S. Park, S. Kim, W. W. Chu. Segment-Based Approach for Subsequence Searches in Sequence Databases, In Proceedings of the 16th ACM Symposium on Applied Computing. 2001 248–252.
Yunfa Hu. Inter-Relevant Successive Trees—An Novel Mathematic Model of Full Text Database. Tech. Rept: CIT-02-3. Computer Science Dept., Fudan University, 2002.3
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, H., Shen, Z., Hu, Y. (2003). Mining Sequence Pattern from Time Series Based on Inter-relevant Successive Trees Model. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_127
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_127
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive