In today's growing market it is very essential to keep track of the interests of the customer and keep them updated on trends and products in the market. In this project we intend to create a shopping portal and have a database that registers the sets of articles that are often bought together using 'APRIORI ALGORITHM'. This information will be used to show ads and offers on the products of your interests. Also to promote new products related to your needs and mainly to suggest and ask about products that are often bought together with the product already present in your shopping cart. The frequently co-occurring group of items is determined by the frequent mining of the model in the databases. Here the main contributing task is to speed up the frequent set of elements by proposing a technique that uses the minimum data available in the shopping cart to predict what other items the customer can choose to buy.
Existing research in association mining has focused mainly on how to accelerate the search for frequently coexisting groups of elements in the type of transaction type transactions; Less attention has been paid to methods that exploit these items of frequent articles for predictive purposes. This work contributes to this latter task by proposing a technique that uses partial information on the contents of a shopping cart to predict what else the customer is likely to buy. Using the recently proposed data structure of itemset trees (IT-trees), we obtain, in a computationally efficient way, all rules whose antecedents contain at least one incomplete cart element. We then combine these rules using uncertainty processing techniques, including classical Bayesian decision theory and a new algorithm based on the Dempster-Shafer (DS) theory of the combination of tests.
Prediction in the shopping cart uses partial information about the contents of a shopping cart to predict what else the customer is likely to buy. In order to reduce the cost of rules mining, we propose a fast algorithm that generates frequent sets of elements without generating sets of candidate elements. The algorithm uses the Boolean vector with the relational Y operation to discover frequent element sets and generate the association rule. Association rules are used to identify relationships between a set of database elements. Initially Boolean Matrix is generated by transforming the database into boolean values. Frequent element sets are generated from the Boolean array. The association rules are then generated from the generated frequent element sets. The association rules generated form the basis for prediction. The set of incoming items, ie the contents of the incoming shopping cart, will also be represented by a boolean vector and the AND operation will be performed with each transaction vector to generate the association rules. Finally the rules are combined to get the predictions.