Mining Positive and Negative Association Rules Using CoherentApproach

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© - Issue 2013 by IJCTT Journal
Volume-4 Issue-1                           
Year of Publication : 2013
Authors :Rakesh Duggirala, P.Narayana.

MLA

Rakesh Duggirala, P.Narayana."Mining Positive and Negative Association Rules Using CoherentApproach"International Journal of Computer Trends and Technology (IJCTT),V4(1):1-8 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract: - —In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Typically, before association rules are mined, a user needs to determine a support threshold in order to obtain only the frequent item sets. Having users to determine a support threshold attracts a number of issues. We propose an association rule mining framework that does not require a pre-set support threshold. Often, the number of association rules, even though large in number, misses some interesting rules and the rules’ quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions. We propose a framework to discover domain knowledge report as coherent rules. Coherent rules are discovered based on the properties of propositional logic, and therefore, requires no background knowledge to generate them. From the coherent rules discovered, association rules can be derived objectively and directly without knowing the level of minimum support threshold required. We provide analysis of the rules compare to those discovered via the apriori. The framework is developed based on implication of propositional logic via Negative and positive association algorithm. The experiments show that our approach is able to identify meaningful association rules within an acceptable execution time. This framework develop a new algorithm based on coherent rules so that users can mine the items without domain knowledge and it can mine the items efficiently when compared to association rules.

References-

[1] Jaiwei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Second Edition , Morgan Kaufmann Publishers.
[2] Feng Yucai, “Association Rules Incremental Updating Algorithm”, Journal of Software, Sept., 1998.
[3] Association Rule Learning – Wikipedia, the free encyclopedia.
[4] Lei Guoping, Dai Minlu, Tan Zefu and Wang Yan, “ The Research of CMMB Wireless Network Analysis Based on Data Mining Association Rules”, IEEE conference paper – project supported by the Science and Technology Research Project of Chongqing municipal education commision under contract no KJ101114 and KJ 111103, 2011
[5] Jayalakshmi.S, Dr k. Nageswara Rao, “Mining Association rules for Large Transactions using New Support and Confidence Measures”, Journal of Theoretical and applied Information Technology, 2005. [6] Hou Sizu, Zhang Xianfei, “ Alarms Association Rules Based on Sequential Pattern Mining Algorithm”, Fifth IEEE International Conference on fuzzy Systems and Knowledge Discovery, 2008.
[6] Srikant, R. & Agrawal, R., “Mining quantitative association rules in large relational tables”, SIGMOD Rec., ACM, 1996, 25, 1-12.

Keywords— ISDA, DDoS, IP Spoofing, TCP/IP, IDS.