Context-based market basket analysis in a multiple-store environment
We propose a new approach to performing market basket analysis in a multiple-store and multiple-period environment. In using the method, the user first defines a time concept hierarchy and a place (location) hierarchy, according to his or her application and needs. A set of contexts is systematically derived from the two hierarchies by combining the concept levels of the two hierarchies. We developed an efficient algorithm for extracting the association rules, which meet the support and confidence requirements for all the contexts. Using the approach, a decision maker can analyze purchasing patterns at very detailed concept levels of time and place, such as a combination of days and stores, at more general levels, such as a combination of quarters and states, and combinations of detailed levels of one with general level of the other, such as a combination of days and regions. In addition to this flexibility, the association rules are well organized, because they are generated according to the contexts derived from the time and place hierarchies. A numerical evaluation shows that the algorithm is efficient in running time and may generate more specific and richer information than the store-chain rules and the traditional rules.
algorithm association rule data mining store chain