Comparative Analysis of Market Basket Analysis through Data Mining Techniques

Abstract

Market basket analysis is a technique for evaluating buyer’s preferences in order to find the connection between various items in the cart. The exploration of these relationships help the vendor to propound the sales strategy by considering the frequent purchased of items and with this kind of approach data-mining techniques best fits in analyzing and implementing the logic. The points of comparisons, which include the concept of buying patterns from the consumer end and the production pattern from the company, end which alternatively helps in procuring or buying the product. Evaluating the activities of business consumers is very important and this can be achieved by various data mining techniques available. This paper provides a comparative study of two widely used data mining techniques in understanding the frequent activities of buyer i.e. Association Rule Mining (ARM) and Collaborative filtering (CF) technique used in product recommendation.