Abstract
The market basket is defined as an itemset purchased together by a customer on a single visit to a store. The market basket analysis is an influential tool for the implementation of store layout and promotional campaign. Particularly in retailing, it is necessary to discover large baskets, since it deals with thousands of items. Most of the previous studies adopt an apriori-like candidate set generation-and-test approach to analyse the market basket date. Although some algorithms can find large itemsets, they can be inefficient in terms of computational time and memory consumption. The aim of this paper is to present new scheme to discover association rules. In addition, a case study in the retailer market is presented to validate the efficiency of the proposed scheme.
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