Predicting Missing Items in Shopping Carts
The Existing researchwork in association mining has focused mainly on how to expedite the search for frequently co-occurring groups of items inshopping cart type of transactions. But less attention has been paid to the use of these frequent itemsets for prediction purposes. In this article is described a method that uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. itemset trees (IT-trees), a recently proposed data structure is used to obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then these rules are combined by uncertainty processing techniques such as the classical Bayesian decision theory and a Dempster-Shafer (DS) theory of evidence combination based new algorithm.

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