08-16-2017, 09:56 PM
Existing research in association mining has focused mainly on the search for frequently co-occurring groups of items in shopping cart type of transactions. This project proposes a technique that uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. Using the recently proposed data structure of item set trees (it-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then, we combine these rules by uncertainty processing techniques, including the classical bayesian decision theory and a new algorithm based on the dempster-shafer (ds) theory of evidence combination
Introduction
THE primary task of association mining is to detect frequently co-occurring groups of items in transactional databases. The intention is to use this knowledge for prediction purposes: if bread, butter, and milk often appear in the same transactions, then the presence of butter and milk in a shopping cart suggests that the customer may also buy bread. More generally, knowing which items a shopping cart contains, we want to predict other items that the customer is likely to add before proceeding to the checkout counter.
Base Paper Presented By:
Kasun Wickramaratna, Student Member, IEE, Miroslav Kubat, Senior Member, IEE, and
Kamal Premaratne, Senior Member, IEE
Key technologies Used in this project: java
Product/ Platform used in this project: Microsoft
full report link
http://doi.ieecomputersociety10.1109/TKDE.2008.229