| dc.description.abstract | In data mining, association rule is used to discover products that co-purchase frequently within customer purchasing transactions. But if product is out of stock or price adjustment, a customer may be to replace the original purchase of some items with that of others. Therefore it exist another relation, substitution rules, in products. The mining of substitution rules, the same as that of association rules, will get very valuable knowledge in decision support.
In this thesis, the mining algorithm for substitution rules is based on the concept of data mining and definition of substitute in the economics. The substitution rules mining algorithm is designed to find out the products that comply with the definition of substitute in the transaction database with four parameter, , , and , for fitting in with real-life.
The substitution rules mining algorithm is implemented and experimented against the real supermarket database. It is shown that the algorithm not only can find out the perfect substitute but also can discover some imperfect substitutes. It is also verify that different branch store and period have distinct substitution rules, and season is more important than position. | en_US |