چکیده (انگلیسی):

Many researches has focused mainly on how to expedite the search for frequently co-occurring groups of items in
“shopping cart” and less attention has been paid to the methods that exploit these “frequent itemsets” for
prediction purposes. This study contributes to this task by proposing a technique that uses the partial information
about the contents of a shopping cart for the prediction of what else the customer is likely to buy. Several
algorithms have been introduced to detect the frequently co occurring group of items in the transactional
databases for prediction purposes. This study presents a new technique whose principal diagonal elements
represent the association among items and looking to the principal diagonal elements, the customer can select
what else the other items can be purchased with the current contents of the shopping cart and also reduces the rule
mining cost. The association among items is shown through Graph. The frequent itemsets are generated from the
Association Matrix. Then association rules are to be generated from the already generated frequent itemsets. We
conducted extensive experiments and showed that the accuracy of our algorithm is higher than the previous
algorithm. Our experiments show that the time needed for predicting the items is highly reduced than other
algorithms. Moreover the memory requirement is also less since our work does not generate candidate itemsets.
In this study, we have successfully implemented the Rule generation technique and predicted the set of other
items that the customer is likely to buy. The performance of our algorithm outperforms the existing algorithm that
needs multiple passes over the database in such a way that it efficiently mines the association among the items in
the shopping cart and the prediction time of the items is greatly reduced.