Impact Factor:6.549
 Scopus Suggested Journal: Tracking ID for this title suggestion is: 55EC484EE39417F0

International Journal
of Computer Engineering in Research Trends (IJCERT)

Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary




Welcome to IJCERT

International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary

ISSN(Online):2349-7084                 Submit Paper    Check Paper Status    Conference Proposal

Back to Current Issues

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.


DOI Link : Not yet assigned

Download :
  V4I504.pdf


Refbacks : Currently there are no ref backs

Support Us


We have kept IJCERT is a free peer-reviewed scientific journal to endorse conservation. We have not put up a paywall to readers, and we do not charge for publishing. But running a monthly journal costs is a lot. While we do have some associates, we still need support to keep the journal flourishing. If our readers help fund it, our future will be more secure.

Quick Links



DOI:10.22362/ijcert


Science Central

Score: 13.30





Submit your paper to editorijcert@gmail.com