An Optimized Association Rule Generation for Market Basket Analysis

M Sathya, K Thangadurai


Data mining has been an influential technique used for analyzing and summarizing data it into useful information. Frequent item set retrieval is a popular data mining technique to study customer behavior in retail stores and medical fields. Several business applications found to benefit from market access. But, there is a need for new effective method to optimize the frequent itemsets and association rule generation. In addition, frequent itemset mining data is utilized to improve the market basket access, to determine the customer behavior, details with suggestion and then to improve point of sales in supermarkets. In this paper we examine Optimization algorithm for generating the optimized association rule to analyze the frequently buying products by customer in supermarkets and to improve sales growth maintenance of supermarkets. The performance of this technique is tested with the metrics such as running time for frequent itemset generation, memory for association rule generation and number of rules generated.

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Sample Dataset for Market Basket Analysis:


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