Browse > Article
http://dx.doi.org/10.3745/KTSDE.2013.2.8.565

Efficient Sequence Pattern Mining Technique for the Removal of Ambiguity in the Interval Patterns Mining  

Kim, Hwan (전남대학교 전자컴퓨터공학부)
Choi, Pilsun (전남대학교 전자컴퓨터공학부)
Kim, Daein (전남대학교 전자컴퓨터공학부)
Hwang, Buhyun (전남대학교 전자컴퓨터공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.8, 2013 , pp. 565-570 More about this Journal
Abstract
Previous researches on mining sequential patterns mainly focused on discovering patterns from the point-based event. Interval events with a time interval occur in the real world that have the start and end point. Existing interval pattern mining methods that discover relationships among interval events based on the Allen operators have some problems. These are that interval patterns having three or more interval events can be interpreted as several meanings. In this paper, we propose the I_TPrefixSpan algorithm, which is an efficient sequence pattern mining technique for removing ambiguity in the Interval Patterns Mining. The proposed algorithm generates event sequences that have no ambiguity. Therefore, the size of generated candidate set can be minimized by searching sequential pattern mining entries that exist only in the event sequence. The performance evaluation shows that the proposed method is more efficient than existing methods.
Keywords
Data Mining; Temporal Pattern; Sequential Patterns; Interval-based Events;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Minos N. Garofalakis, Rajeev Rastogi, Kyuseok Shim, "SPRIT : Sequential Pattern Mining with Regular Expression Constraints", Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, pp.223-234, 1999.
2 K. Y. Huang, C. H. Chang, "SMCA : A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases", IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6, 2005. 6.
3 Y. P. Huang, L. J. Kao, F, E. Sandnes, "A Prefix Tree-Based Model for Mining Association Rules from Quantitative Temporal Data". IEEE International Conference on Systems, Man, and Cybernetics, Vol.1, pp.158-163, 2005. 10.
4 Y. J. Lee, J. W. Lee, D. J. Chai, B. H. Hwang, K. H. Ryu, "Mining temporal interval relational rules from temporal data", The Journal of Systems and Software, 82(2009), 155-167.   DOI   ScienceOn
5 P.S. Kam and A.W.C. Fu, "Discovering Temporal Patterns for Interval-Based Events", Proc. Second Int'l Conf. Data Warehousing and Knowledge Discovery (DaWaK '00), 2000.
6 J.F. Allen, "Maintaining Knowledge about Temporal Intervals", Comm. ACM, Vol.26, No.11, pp.832-843, 1983.   DOI   ScienceOn
7 Shin-Yi Wu, Yen-Liang Chen, "Mining Nonambiguous Temporal Patterns for Interval-Based Events", IEEE Transactions on Knowledge and Data Engineering, Vol.19, No.6, June, 2007.
8 M.-S. Chen, J. Han, and P.S. Yu, "Data Mining: An Overview from a Database Perspective," IEEE Trans. Knowledge and Data Eng., Vol.8, No.6, pp.866-883, Dec., 1996.   DOI   ScienceOn
9 W.J. Frawley, G. Piatetsky-Shapiro, and C.J. Matheus, Knowledge Discovery in Database: An Overview. AAAI/MIT Press, 1991.
10 J. Han and M. Kamber, Data Mining: Concepts and Techniques. Academic Press, 2001.
11 R. Srikant, R. Agrawal, "Mining sequential patterns : generalizations and performance improvements", Proceedings of International conference, on Extendng Database Technology, Avignon, France. Springer-Verlag. 1996.
12 Jian Pei, Jiawei Han, B. Mortazavi-Asi, J. Wang, H. Pinto, Q. Chen, U. Dayal, M. Hsu, "Mining Sequential Patterns by Pattern-Growth", The PrefixSpan Approach, IEEE Transactions on Knowledge and Data Engineering, Vol.16, 2004. 11.
13 J. Allen, "Maintaining Knowledge about Temporal Intervals", Comm. of the ACM, Vol.26(11), 1983. 11.
14 Y. L. Chen, S. Y. Wu, "Mining temporal patterns from sequence database of interval-based events", Int. Conference on Fuzzy Systems and Knowledge Discovery, Xian, China, 2006.