Constraint data mining using apriori algorithm with AND operation

Data mining is one of the most important steps in knowledge discovery. Apriori algorithm is the most used one in this process. The major drawback with Apriori algorithm is of time and space. It generates numerous uninteresting itemsets which lead to generate various rules which are of completely of no use. The two factors considered for association rules generation are Minimum Support Threshold and Minimum Confidence Threshold. However, constraint mining reduces these two limitations of Apriori algorithm to a considerable extent. This paper uses constraint mining and AND operation between MST and MCT to prune itemsets generated in each iteration. The overall performance has been increased and simulated in this paper through various figures from simulation result.