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Companies these days rely strongly upon their previous data history for predicting future trends in their business operations and strategies. Whether forecasting inventory or estimating sales, data mining has emerged as a new vibrant tool for extracting potential knowledge hidden inside the purchase behavior of the customer. Mining has made it possible to span over a large dataset in no time and come up with useful prediction. Building on the notion that customer’s purchase pattern plays a vital role in planning future strategies, in this paper, we focus upon several mining techniques used for understanding the customer’s purchase behavior. We analyze the purchase behavior from three different mining aspects i.e. classification, association rules, clustering technique and compare their accuracies. Thereafter, the study builds upon a scope whereby these techniques can be used to achieve a much filtered and unique dataset for analysis and also can be used for product recommendation for new customers. Finally, the study lays the scope of getting these techniques to be in sync with techniques used in forecasting such that companies can plan their sales and conduct the inventory management effectively. General Terms Mining of customer purchase behavior and recommendation of products.

Evaluating Techniques for Mining Customer Purchase Behavior and Product Recommendation- A Survey