Journal Articles (All Issues)

ASSOCIATION RULES WITHOUT REDUNDANCY

Authors

Amit Kumar Chandanan1* , Suman Singh2

Keyword Association Rule, HBPSO, Mutation, non-redundant rule

Abstract

Association rules are one of the most researched areas of data mining. This is useful in the marketing and retailing strategies. Association mining is to retrieval of a set of attributes shared with a large number of objects in a given database. There are many potential application areas for association rule approach which include design, layout, and customer segregation and so on. The redundancy in association rules affects the quality of the information presented. The goal of redundancy elimination is to improve the quality and usefulness of the rules. The aim of this work is to remove hierarchical duplicity in multi-level, thus reducing the size of the rules set to improve the quality and usefulness without any loss. Redundancy in association rules mining decreases the speed for rules generation. It causes so many rules to be generated for same set of attribute. This research proposes a hybrid method that is combination of binary particle swarm optimization and mutation i.e. HBPSO.

References

    Agrawal R. and Srikant R. (1995). Mining sequential patterns. Proceedings of the Eleventh International Conference on Data Engineering. Ceglar A. and J F Roddick J.F. (2006). Association Mining. ACM Computing Surveys (C SUR). Vol 38(2): p. 13. Tanna P. and Y Ghodasara Y. (2013). Foundation for Frequent Pattern Mining Algorithms Implementation. International Journal of Computer Trends and Technology (IJCTT). Vol 4(7). Han J, Y Fu (2000). Mining Multiple Level Association Rules in Large Databases. IEEE Transactions on Knowledge and Data Engineering Vol 11(5): p.798-804 11 Zaki M. J. (2004). Mining Non-Redundant Association Rules. Data Mining and Knowledge. Vol 9: p. 223-248. Chandanan A. K. and Shukla M. K. (2013). Review on Redundancy Free Association Rule mining. IJCA Proceedings on International Conference on Communication Technology ICCT (5):13-16. Shrivastava N. and Singh S. L. (2012). Overview of Non-Redundant Association Rule Mining. Research Journal of Recent Sciences. Vol. 1(2): ISSN 2277-2502: p.108-112.

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Published

2021-12-30

Issue

Vol. 40 No. 12 (2021)