Aggregate Function Based Enhanced Apriori Algorithm for Mining Association Rules

Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rules strength, while support value corresponds to statistical significance. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. To replace the Aprori's user defined minimum threshold value, this paper proposes an aggregate function based on Central Limit Theorem CLT that calculates a more meaningful minimum threshold value. The paper also proposes a new function, Specified Minimum Support value function with bit mapping, which calculates a custom minimum support for each item set based on the probability of collision chance of its items. Furthermore, a modification for Apriori algorithm to accommodate this function is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.