nVApriori : A novel approach to avoid irrelevant rules in association rule mining using n-cross validation technique

Association rule mining finds interesting associations or correlations in a large pool of transactions. Apriori based algorithms are two step algorithms for mining association rules from large datasets. They find the frequent item sets from transactions as the first step and then construct the association rules. Though these algorithms generate multiple rules, most of the rules become irrelevant to the transactions. The exercise becomes costly in terms of memory usage and decision making is also not precise. This research addresses this drawback by developing ways to reduce irrelevant rules. This paper proposes the ncross validation technique to filter such irrelevant rules. The proposed algorithm is called nVApriori (n-cross Validation based Apriori) algorithm. The proposed nVApriori algorithm uses a partition based approach to support the association rule validations. The proposed nVApriori algorithm has been tested with two synthetic datasets and two real datasets. The performance analysis is compared with Apriori, most frequent rule mining algorithm and non redundant rule mining algorithm to study the efficiency. This proposed work aims at reducing a large number of irrelevant rules and produces a new set of rules having high levels of confidence.