Luís Cavique †
† ESCS-IPL / IST-UTL Portugal
Abstract:
The market basket is defined as an itemset bought together by a customer on a single visit to a store. The market basket analysis is a powerful tool for the implementation of cross-selling strategies. Although some algorithms can find the market basket, they can be inefficient in computational time. The aim of this paper is to present a faster algorithm for the market basket analysis using data-condensed structures. In this innovative approach, the condensed data is obtained by transforming the market basket problem in a maximum-weighted clique problem. Firstly, the input data set is transformed into a graph-based structure and then the maximum-weighted clique problem is solved using a meta-heuristic approach in order to find the most frequent itemsets. The computational results show accurate solutions with reduced computational times.
Keywords: data mining, market basket, similarity measures, maximum clique problem.
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