Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31742
Discovery of Sequential Patterns Based On Constraint Patterns

Authors: Shigeaki Sakurai, Youichi Kitahata, Ryohei Orihara


This paper proposes a method that discovers sequential patterns corresponding to user-s interests from sequential data. This method expresses the interests as constraint patterns. The constraint patterns can define relationships among attributes of the items composing the data. The method recursively decomposes the constraint patterns into constraint subpatterns. The method evaluates the constraint subpatterns in order to efficiently discover sequential patterns satisfying the constraint patterns. Also, this paper applies the method to the sequential data composed of stock price indexes and verifies its effectiveness through comparing it with a method without using the constraint patterns.

Keywords: Sequential pattern mining, Constraint pattern, Attribute constraint, Stock price indexes

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1227


[1] R. Agrawal and R. Srikant, Mining Sequential Patterns, Proc. of the 11th Intl. Conf. Data Engineering, 1995, Taipei, Taiwan, pp. 3-14.
[2] J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, "Sequential pattern mining using a bitmap representation," Proc. of the 8th Intl. Conf. on Knowledge Discovery and Data Mining, 2002, Edmonton, Alberta, Canada, pp. 429- 435.
[3] M. N. Garofalakis, R. Rastogi, and K. Shim, "SPIRIT: Sequential Pattern Mining with Regular Expression Constraints," Proc. of the Very Large Data Bases Conf., 1999, Edinburgh, Scotland, UK, pp. 223-234.
[4] J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, M. -C. Hsu, "Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach," IEEE Trans. on Knowledge and Data Engineering, vol. 16, no. 11, pp. 1424-1440, 2004.
[5] J. Pei, J. Han, and W. Wang, "Mining Sequential Patterns with Constraints in Large Databases," Proc. of the 11th ACM Intl. Conf. on Information and Knowledge Management, 2002, McLean, Virginia, USA, pp. 18-25.
[6] S. Sakurai, K. Ueno, and R. Orihara, "Discovery of Time Series Event Patterns based on Time Constraints from Textual Data," Intl. J. of Computational Intelligence, vol. 4, no. 2, pp. 144-151, 2008.
[7] S. Sakurai, Y. Kitahara, R. Orihara, K. Iwata, N. Honda, and T. Hayashi, "Discovery of Sequential Patterns Coinciding with Analysts- Interests," J. of Computers, vol. 3, issue 7, pp. 1-8, 2008.
[8] R. Srikant and R. Agrawal, "Mining Sequential Patterns: Generalizations and Performance Improvements," Proc. of the 5th Intl. Conf. Extending Database Technology, 1996, Avignon, France, pp. 3-17.
[9] Z. Yang and M. Kitsuregawa, "LAPIN-SPAM: An Improved Algorithm for Mining Sequential Pattern," Proc. of the 21st Intl. Conf. on Data Engineering Workshops, 2005, Tokyo, Japan, pp. 1222.
[10] M. J. Zaki, "SPADE: An Efficient Algorithm for Mining Frequent Sequences," Machine Learning, vol. 42, no. 1/2, pp, 31-60, 2001.