An approach for retail goods association rules analysis with memory property

The market basket analysis based on association rules is a powerful tool for physical and online retailers to stimulate consumption and increase profits via improving strategies on category management, store layout, associated promotions and recommendations. According to this demand, we propose AC algorism to get association rules and M-AC algorism which is used to obtain frequent k-itemset based on adjacency matrix and cut matrix technology. This method possesses such merits as simple and efficient operation, low cost, and memory property (meaning that there is no need to operate again when data updates). The effectiveness of this new method is proved by its successful application in a retail chains.