Mining hidden pattern from existing databases is an important topic in field of data mining. The knowledge obtained from these databases is used in different applications like in market basket analysis. Association Rules are important to discover the relationships among the attributes in a database. In general the rules generated by Association Rule Mining technique do not consider the negative occurrences of attributes in them, but by focusing on infrequent items generated in system we can predict the rules which contains negative attributes. This paper proposes an improved algorithm NAPSO based on Particle Swarm Optimization. The algorithm improves result provided by apriori algorithm.

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In the Proc. of the ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD '93), Washington, USA, May 1993.
2. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. InProc. of the3rdInt'l Conf. on Information and Knowledge Management, Gaithersburg, Maryland, 29. Nov - 2. Dec 1994.
3. C. Silverstein, S. Brin, R. Motwani and J. D. Ullan. Scalable techniques for mining causal structures. In the Proc. of 1998 ACM SIGMOD Int'l Conf. on Management of Data, Seattle, Washington, USA, June 1998.
4. S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalising association rules to correlations. In the Proc. of the ACM SIGMOD Int'l Conference on Management of Data (ACM SIGMOD '97).
5. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In the Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, 1997.
6. M. L. Antonie, and O. R. Zaiane, "Mining Positive and Negative Association Rules: An Appraoch for Confined rules", Proc. Of the 8-th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD), Pisa, Italy, pp. 27-38, 2004.
7. H. Zhu, and Zh. Xu, "An Effective Algorithm for Mining Positive and Negative Association Rules", Proc. Of the 2008 Intl. Conf. on Computer Science and Software Engineering (CSSE), Wuhan, China, pp. 455-458, 2008.
8. S. Brin, R. Motwani, and C. Silverstein, "Beyond market basket: Generalizing association rules to correlations", Proc. of 1997 ACM SIGMOD Intl. Conf. Management of Data, ACM, Tucson, Arizona, USA, pp. 265-276, 1997.
9. A. Savasere, E. Omiecinski. and S. Navathe, "Mining for Strong Negative Associations in a Large Database of Customer Transactions", Proc. vOf the 1998 Intl. Conf. on Data Engineering (ICDE), pp. 494-502, 1998.
10. M. Gan, M. Y. Zhang, and Sh. W. Wang, "One Extended Form For Negative Association rules and the Corresponding Mining Algorithm", Proc. Of the 4-th Intl. Conf. on Machine Learning and Cybernetics, Guangzhou, pp. 1716-1721, 2005.
11. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings of the 20th International Conference on Very Large Databases, VLDB, September 1994, pp. 487–499.
12. J. Kennedy, and R. Eberhart,"Particle Swarm Optimization,"Proceedings of the IEEE conference on neural networks – ICNN'95,vol. IV, Perth, Australia, 1995, pp. 1942-1948.
13. Chi-Yang Tsai, I-Wei Kao "Particle swarm optimization with selective particle regeneration for data clustering". Expert Systems withApplications 2010.
14. Ahmad Nickabadi, Mohammad Mehdi Ebadzadeh, Reza Safabakhsh "A novel particle swarm optimization algorithm with adaptive inertia weight" Applied Soft Computing 2011.

Association Rule Mining (arm) Data Mining (dm) Negative Association Rule (nar) Particle Swarm Optimization (pso)