In modern world, analyzing data and extracting useful information from the data is one of the crucial task in business analysis. Now, extracting patterns from the data has occurred from centuries. Bayes theorem (used in the 1700s), Regression analysis (used in the 1800s) were the earlier process of identifying and extracting pattern from a huge collection of relevant data. In this regard, association rule learning is one of the popular, well researched procedures to extract pattern or rules to gather information from huge relevant data. In this paper, a new approach of segregating data and generating rules under the framework of binary transaction based modified enhanced Apriori Algorithm have been presented. This algorithm can be applied in any number of data efficiently. Apart from Market Basket Analysis, this algorithm can be applied in the field of web usage mining, intrusion detection, bioinformatics etc.

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Apriori Algorithm Association Rules Itemset Binary Transaction Supports Count.