6th International Young Scientist Congress (IYSC-2020) will be Postponed to 8th and 9th May 2021 Due to COVID-19. 10th International Science Congress (ISC-2020).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Overview of Non-redundant Association Rule Mining

Author Affiliations

  • 1IES, IPS Academy Indore, MP, INDIA

Res. J. Recent Sci., Volume 1, Issue (2), Pages 108-112, February,2 (2012)

Abstract

Sequential association rule mining is one of the possible methods to analysis of data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules, because of the complexity and temporal ordered characteristics of sequential data, current research of sequential rule mining is limited. Although several sequential association rule prediction model using either sequence constraint or temporal constraint have been proposed, none of them considered the redundancy problem in rule mining. The main purpose of this paper to propose a non redundant sequential association rule mining method proposed the Sequential Min-Max basis for concise representation of non-redundant sequential association rules.

References

  1. Ayres J., Flannick J., Gehrke J. and Yiu T., Sequential PAttern Mining using a Bitmap Representation. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)
  2. Agrawal R. and Srikant R., Fast algorithms for mining association rules in large databases, Proceedings of 20th International Conference on Very Large Databases (1994)
  3. Desikan P., Pathak N., Srivastava J. and Kumar V., Incremental page rank computation on evolving graphs. Paper presented at the Special interest tracks and posters of the 14th International Conference on World Wide Web (2005)
  4. Ganter B. and Wille R., Formal Concept Analysis: Mathematical Foundations, Springer, Berlin-Heidelberg-New York, 10,(1999)
  5. Gaul W. and Schmidt-Thieme L., Mining Generalized Association Rules for Sequential and Path Data, Proceedings of the 2001 IEEE International Conference on Data Mining (2001)
  6. Agrawal R., Imielinski T. and Swami A., Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (1993)
  7. Agrawal R. and Srikant R., Mining sequential patterns, Proceedings of the Eleventh International Conference on Data Engineering 1995 (1995)
  8. Ashrafi M.Z., Taniar D. and Smith K., Redundant association rules reduction techniques, International Journal of Business Intelligence and Data Mining (2007)
  9. Guo S., Liang Y., Zhang Z. and Liu W., Association Rule Retrieved from Web Log Based on Rough Set Theory. Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 03, (2007)
  10. Brin S., Motwani R., Ullman J.D., and Tsur S., Dynamic item set counting and implication rules for market basket data, In SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, 255-264 (1997)