Analyzing Online Transaction Data using Association Rule Mining

Abstract

Association Rule Mining is a data mining designed to discover the real connections of data items in transaction data build on associativity. The technique utilizes the Apriori Algorithm to discover association rules. Furthermore, the Apriori Algorithm widely treated to discover frequent itemsets in transaction data. This study aims to enhance the efficiency of the Apriori Algorithm in the mining of association rule as a reference to identifying mixed item deals as a regular promo to offer to customers build on item frequency of buying. The study shows that Association rule mining implementation through enhanced Apriori Algorithm generates results at a higher performance rate or lesser runtime rate compared with the original Apriori Algorithm, and it helps the organization in selecting customer product deals. Anticipated in business flow, the study produced a list of package items for consumers based on strong rules generated by association rule mining at a lesser runtime rate.