The market basket analysis is a powerful tool especially in retailing as it deals with thousands of items and it becomes essential to discover large baskets. Market basket analysis analyzes the buying habits of customers as to which products are tend to be purchased together. The discovery of these associations helps the retailers to develop marketing strategies, make business decisions and maximize the profits. Several strategies have been proposed to push several types of constraints within the most well known algorithms in the context of mining frequent itemsets. In the present paper an effective FP-Bonsai algorithm is implemented for mining frequent patterns. According to the mining result, the products are arranged together to well-suite the customer's needs and interests. FP-bonsai algorithm results in a very efficient frequent itemset mining algorithm that effectively exploits monotone constraints. The use of FP-bonsai instead of the traditional FP-growth algorithm increases the efficiency and reduces the execution time to discover the frequent patterns.
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