CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Review of Modern Sequential Rule Mining Techniques

by Pushpraj Patel, Mahesh Malviya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 6
Year of Publication: 2014
Authors: Pushpraj Patel, Mahesh Malviya
10.5120/15359-3829

Pushpraj Patel, Mahesh Malviya . A Review of Modern Sequential Rule Mining Techniques. International Journal of Computer Applications. 88, 6 ( February 2014), 32-35. DOI=10.5120/15359-3829

@article{ 10.5120/15359-3829,
author = { Pushpraj Patel, Mahesh Malviya },
title = { A Review of Modern Sequential Rule Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 6 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number6/15359-3829/ },
doi = { 10.5120/15359-3829 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:06:57.064386+05:30
%A Pushpraj Patel
%A Mahesh Malviya
%T A Review of Modern Sequential Rule Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 6
%P 32-35
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sequential rule mining is an important data mining task with wide applications. The current algorithms for discovering sequential rules common to several sequences use very restrictive definitions of sequential rules. Among various data mining objectives the mining of frequent patterns has been the focus of knowledge discovery in databases. In this paper, we aim to investigate efficient algorithm for mining including association rule and sequential patterns. The time and space consumption of proposed algorithm will be lesser in comparison to previous algorithm. From the broad variety of efficient algorithm that has been developed we will compare the most important ones. We will analyze the performance of various algorithms on the basis of both their run time performance and theoretical considerations. We use pattern growth approach for discovering valid rules such that it can be much more efficient and scalable.

References
  1. Rakesh Agrawal, Swami, A. , & T. Imielminski, 1993, Mining Association Rules Between Sets of Items in Large Databases, SIGMOD Conference, pp. 207-216
  2. Rakesh Agrawal, & Ramakrishnan Srikant, 1995, Mining Sequential Patterns. Proc. Int. Conf. on Data Engineering, pp. 3-14.
  3. Han, J. Cheung, Wong, Y. , , Ng. V. , & D. W. 1996, Maintenance of discovered association rules in large databases: An incremental updating technique. Proc. ICDE 1996, 106-114.
  4. King Ip Lin. , Heikki Mannila, Gautam Das, Gopal Renganathan, & Padhraic Smyth, 1998. Rule Discovery from Time Series. Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining.
  5. Liying Jiang & Jiternder S Deogun, 2005. Prediction Mining – An Approach to Mining Association Rules for Prediction. Proceeding of RSFDGrC 2005 Conference, pp. 98-108.
  6. Faghihi, U. , Fournier-Viger, P. , Nkambou, R. & Poirier, P. , 2010. The Combination of a Causal Learning and an Emotional Learning Mechanism for Improved Cognitive Tutoring Agent. Proceedings of IEA-AIE 2010 (in press).
  7. Kabanza, F. , Nkambou, R. & Belghith, K. 2005. Path-planning for Autonomous Training on Robot Manipulators in Space. Proc. 19th Intern. Joint Conf. on Artificial Intelligence, 35-38.
  8. Hamilton, H. J. & Karimi, K. 2005. The TIMERS II Algorithm for the Discovery of Causality. Proc. 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 744-750.
  9. Harms, S. K. , Deogun, J. & Tadesse, T. 2002. Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences. Proc. 13th Int. Symp. on Methodologies for Intelligent Systems, pp. . 373- 376.
  10. Hegland, M. 2007. The Apriori Algorithm – A Tutorial. Mathematics and Computation. Imaging Science and Information Processing, 11:209-262.
  11. Hsieh, Y. L. , Yang, D. -L. & Wu, J. 2006. Using Data Mining to Study Upstream and Downstream Causal Realtionship in Stock Market. Proc. 2006 Joint Conference on Information Sciences.
  12. Laxman, S. & Sastry, P. 2006. A survey of temporal data mining. Sadhana 3: 173-198.
  13. Mannila, H. , Toivonen & H. , Verkano, A. I. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(1):259-289.
  14. Gregory Piatetsky-Shapiro and William Frawley, Discovery in Databases, AAAI/MIT Press, 1991.
  15. Rakesh Agrawal, and Ramakrishnan Srikant, 1994. "Fast Algorithms for Mining Association Rules", In Proceedings of the 20th Int. Conf. Very Large Data Bases, pp. 487-499.
  16. Rakesh Agrawal, & Ramakrishnan Srikant. , 1995. "Mining generalized association rules". In: Dayal U, Gray P M D, Nishio Seds. Proceedings of the International Conference on Very Large Databases. San Francisco, CA: Morgan Kanfman Press, pp. 406-419.
  17. M. Houtsma, and Arun Swami, 1995. "Set-Oriented Mining for Association Rules in Relational Databases". IEEE International Conference on Data Engineering, pp. 25–33.
  18. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, 1997. "Dynamic itemset counting and implication rules for market basket data". In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, volume 26(2) of SIGMOD Record, pp. 255–264. ACM Press.
  19. Chen, E. , Cao, H. , Li, Q. , & Qian, T. (2008). Efficient strategies for tough aggregate constraint-based sequential pattern mining. Inf. Sci. , 178(6), 1498-1518.
  20. Masseglia, F. , Poncelet, P. , & Teisseire, M. (2003). Incremental mining of sequential patterns in large databases. Data Knowl. Eng. , 46(1), 97–121.
  21. Wang, J. L. , Chirn, G. , Marr, T. , Shapiro, B. , Shasha, D. , & Zhang, K. (1994). Combinatorial pattern discovery for scientific data: Some preliminary results. Proc. ACM SIGMOD Int'l Conf. Management of Data, (pp. 115-125).
  22. Yang, J. , Wang, W. , & Yu, P. S. (2001). Infominer: mining surprising periodic patterns. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  23. Srikant, R. ,& Agrawal, R. (1996). Mining Sequential Patterns: Generalizations and Performance Improvements. Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology.
  24. Philippe Fournier-Viger,Usef Faghihi,Roger Nkambou, Engelbert Mephu Nguifo-"CMRULES: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences"
Index Terms

Computer Science
Information Sciences

Keywords

Sequential Rules Data Mining Association Rules

Powered by PhDFocusTM