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Survey on Mining Partially Ordered Sequential Rules

Mr. Sandipkumar Sagare, Prof. Suresh Shirgave,

Affiliations
M.E.C.S.E Student, C.S.E Department, D.K.T.E. Society’s Textile and Engineering Institute,Ichalkaranji, 416115
:10.22362/ijcert/2017/v4/i5/xxxx [UNDER PROCESS]


Abstract
Nowadays in various applications such as stock market analysis, e-commerce sequential rule mining is used to extract important data. It majorly includes identification of common multiple sequential rules from given sequence database. One of the general forms of sequential rule mining is Partially Ordered Sequential rules in which listed items in left and right side of rule does not need to be ordered. These partially ordered sequential rules are identified using RuleGrowth Algorithm, TRuleGrowth Algorithm. These algorithms identify partially ordered sequential rules for more generalized decision making. In this paper we are focusing on such algorithms.


Citation
Sandipkumar Sagare et.al, “Survey on Mining Partially Ordered Sequential Rules”, International Journal of Computer Engineering In Research Trends, 4(5):169-170 ,May -2017.


Keywords : Sequential rules, sequential patterns, temporal patterns, pattern mining, sequence, data mining.

References
1. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q.
Chen, U. Dayal, and M. Hsu, ―Mining sequential
patterns by pattern-growth: The pre-fixspan
approach,‖ IEEE Trans. Knowl. Data Eng., vol. 16,
no. 10, pp. 1–17, Oct. 2004.
2. R. Agrawal and R. Srikant, ―Mining sequential
patterns,‖ in Proc. 11th Int. Conf. Data Eng., 1995,
pp. 3–14.
3. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q.
Chen, U. Dayal, and M. Hsu, ―Mining sequential
patterns by pattern-growth: The pre-fixspan
approach,‖ IEEE Trans. Knowl. Data Eng., vol. 16,
no. 10, pp. 1–17, Oct. 2004.
4. M. J. Zaki, ―SPADE: An efficient algorithm for
mining frequent sequences,‖ Mach. Learning, vol. 42,
no. 1–2, pp. 31–60, 2001.pp. 1–17, Oct. 2004.
5. D. Lo, S.-.C. Khoo, and L. Wong, ―Non-redundant
sequential rules—Theory and algorithm,‖ Inf. Syst.,
vol. 34, no. 4/ 5, pp. 438–453, 2009.
6. Y. Zhao, H. Zhang, L. Cao, C. Zhang, and H.
Bohlscheid, ―Mining both positive and negative
impact-oriented sequential rules from transactional
data,‖ in Proc. 13th Pacific-Asia Conf.


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DOI:10.22362/ijcert


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