Author : Babita Rani and Shruti Aggarwal

Data Mining is used to discover the knowledge from large amount of databases and transform it into a flexible structure. Association rule mining (ARM) is the essential part of data mining process. Finding good quality of association rules between items in large databases has been an important and challenging association rule mining problem. The rules mined through ARM algorithms are used for decision making. The good quality of rules helps in better decision making. Optimization of apriori algorithm to generate strong association rules so that good qualities of rules are mined. Apriori algorithm is used to generate all significant association rules between items in the database. On the basis of Association Rule Mining and Apriori Algorithm, a new algorithm is proposed based on the Ant Colony Optimization algorithm to improve the result of association rule mining. Ant Colony Optimization (ACO) is a meta-heuristic approach and inspired by the real behaviour of ant colonies. First association rules generated by Apriori algorithm then find the rules from weakest set based on the threshold value and used the Ant Colony algorithm to reduce the association rules and discover the better quality of rules than apriori. The research work proposed focuses on reducing the scans of databases by optimization and improving the quality of rules generated for ACO.

Keywords: Data Mining, Association Rule Mining (ARM), Apriori Algorithm, Ant Colony Optimization(ACO), FP-Growth.

Article published in International Journal of Current  Engineering  and Technology, Vol.3,No.5(Dec- 2013)