In data mining, there are several works proposed for mining the association rules which are frequent. Researchers argue that mining the infrequent item sets are also important in certain applications. Discovering association rules are based on the preset minimum support threshold given by domain experts. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Even though the number of association rules is large, some interesting rules will be missing and the rules quality requires further analysis. As a result, decision making using these rules could lead to risky actions. Here the focus is mainly on mining both the frequent and infrequent association rules which are more interesting and does not have redundant rules. This is based on predefined rules formed using propositional logic and then the predefined rules are processed by comparing with elements in the actual dataset. The association rules which are obtained will not have redundancies and they will be logically correct. Generalized association rules will be obtained if single level mining is performed. These rules can only help in very high level decision making. In order to allow for in-depth decision making, more specific association rules are obtained. Therefore multiple level mining processes is employed here.

1. Agrawal R., Imielinski T. and Swami A. (1993) “Mining Association Rules between Sets of Items in Large Databases” SIGMOD Record, vol. 22, pp. 207-216.
2. Alex TzeHiangSim, Maria Indrawan, SamarZutshi and BalaSrinivasan(2010) “Logic Based Pattern Discovery” IEEE Transactions on Knowledge and Data Engineering, VOL. 22, NO. 6, pp.798-811.
3. Antonie M.-L. and Zaýane O.R. (2004) “Mining Positive and Negative Association Rules: An Approach for Confined Rules” Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD ’04), pp. 27-38.
4. Bing Liu, Minqing Hu, and Wynne Hsu (2000) “Multi-Level Organization and Summarization of the Discovered Rules” Proc. ACM SIGKDD, Aug 20-23.
5. Blanchard J., Guillet F., Briand H. and Gras R. (2005) “Assessing Rule Interestingness with a Probabilistic Measure of Deviation from Equilibrium” Proc. 11th Int’l Symp. Applied Stochastic Models and Data Analysis (ASMDA ’05), pp. 191-200.
6. Brin S., Motwani R. and Silverstein C. (1997) “Beyond Market Baskets: Generalizing Association Rules to Correlations” Proc. 1997 ACM SIGMOD, pp. 265-276.
7. Cao Longbing (2008) “Introduction to Domain Driven Data Mining,” Data Mining for Business Applications, Springer, pp. 3-10.
8. Choonho Kim and Juntae Kim (2003) ”A Recommendation Algorithm Using Multi-Level Association Rules” Proc. IEEE/WIC Int’l Conf. on Web Intelligence page. 52.
9. Hellerstein J.L., Ma S., Perng C (2002) ”Discovering actionable patterns in event data” IBM Systems Journal, Vol 41, NO 3.
10. KanimozhiSelvi C.S. and Tamilarasi A. (2009) ”An Automated Association Rule Mining Technique With Cumulative Support Thresholds”Int. J. Open Problems in Compt. Math, Vol. 2, No. 3.
11. Koh Y.S., Rountree N. and O’Keefe R.A (2006) “Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse” Int’l J. Data Warehousing and Mining, vol. 2, pp. 38-54.
12. Laszlo Szathmary, Amedeo Napoli and PetkoValtchev (2007) “Towards Rare Itemset Mining” Proc. of the 19th IEEE ICTAI, Vol. 1,pp. 305-312.
13. Li J. and Zhang Y. (2003) “Direct Interesting Rule Generation” Proc. Third IEEE Int’l Conf. Data Mining, pp. 155-162.

Association Rule Multilevel Association Rule Mining concept hierarchy