Abstract
In this paper, we address a data-mining problem that is the discovery of local sequential patterns from a set of long sequences. Each local sequential pattern is represented by a pattern A→B and a time period in which A→B is frequent. Such patterns are actually very common in practice and are potentially very useful. However it is impractical to use traditional methods on this problem directly. We propose a suffix-tree-like data structure for indexing the instances of the patterns. Based on this index, our mining method can discover all locally frequent patterns after one scan of the sequences. We have analyzed the behavior of the problem and evaluated the performance of our algorithm with both synthetic and real data. The results correspond with the definition of the problem and verify the superiority of our approach.
The research has been supported in part of Chinese national key fundamental research program (no, G1998030414) and Chinese national fund of natural science (no. 79990580)
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References
P. Weiner Linear pattern matching algorithms. Conference Record, the IEEE 14th Annual Symposium on Switching and Automata Theory, 1973.
K. Wang, Discovering patterns from large and dynamic sequential data, Special Issues on Data Mining and Knowledge Discovery, Journal of Intelligent Information Systems, 9(1), 8–33, 1997.
R. Agrawal and R. Srikant Mining sequential patterns. In Proc. of International Conference On Data Engineering. Taipei, 1995.
R. Srikant, R. Agrawal Mining sequential patterns: generalizations and performance improvements. The Fifth International Conference on Extending Database Technology. 1996.
K. Wang and J. Tan Incremental discovery of sequential patterns. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada. 1996.
H. Mannila and H. Toivonen Discovering generalised episodes using minimal occurences. Second International Conference on Knowledge Discovery and Data Mining (KDD-96). 1996.
P.-s. Kam and A. W.-C. Fu Discovering temporal patterns for interval-based events. Second International Conference on Data Warehousing and Knowledge Discovery. 2000.
Y. Li, X.S. Wang and S. Jajodia Discovering temporal patterns in multiple granularities. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining. Lyon, France. 2000.
A. Tansel, N. Ayan Discovery of association rules in temporal databases. 4th International Conference on Knowledge Discovery and Data Mining (KDD’98) Distributed Data Mining Workshop, New York, USA, August 1998.
G. Das, K. Lin, H. Mannila, G. Renganathan and P. Smyth Rule discovery from time series. the 4th International Conference on KDD. 1998.
M. Spiliopoulou and J.F. Roddick Higher order mining: modelling and mining the results of knowledge discovery. Data Mining II-Second International Conference on Data Mining Methods and Databases. 2000.
X. Chen, I. Petrounias An Integrated query and mining system for temporal association rules. The 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000), London, UK. 327–336. 2000.
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Jin, X., Lu, Y., Shi, C. (2002). Discovering Local Patterns from Multiple Temporal Sequences. In: Shafazand, H., Tjoa, A.M. (eds) EurAsia-ICT 2002: Information and Communication Technology. EurAsia-ICT 2002. Lecture Notes in Computer Science, vol 2510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36087-5_4
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DOI: https://doi.org/10.1007/3-540-36087-5_4
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