A novel approach for discovering retail knowledge with price information from transaction databases

With the advances in information technology and the emergence of Internet commerce, analysis of transaction data has become a crucial technique for effective decision-making and strategy formation in business operations. It is especially critical for retail management, in both online and brick-and-mortar stores. Traditional research in mining retail knowledge, however, does not take into account the products’ prices and how such settings can affect potential demand. This paper opens a new research dimension by treating products’ prices as an important decision variable in mining retail knowledge. To the best of our knowledge, the problem addressed in this paper has never been dealt with in existing research papers. We propose a representation scheme to incorporate price information into historical transaction data. An efficient algorithm is developed to “dig” out implicit, yet meaningful, patterns with price information. In addition, an extensive and well-designed experiment is executed, showing that the algorithm is computationally efficient and that the proposed analysis is significant and useful.