Combined Item Sets Generation using Modified Apriori Algorithm

Abstract

Association rule mining is used to find association relationships among data sets. Apriori algorithm is one of the classical algorithms of association rule mining. It generates the association rules from transaction data, such as, if item 'a' is bought then what are the chances to buy item 'b'. It uses support and confidence values to generate the association rule. In this paper, we modified the classical apriori algorithm in such way that so we can generate item sets as a package, which have higher possibility to buy together by the customers. To generate these packages, we introduced a new combined support value of the items sets. This combined support value is used along with the apriori algorithm to generate package items within a minimum support value. The generated item sets can also help the decision maker to forming new packages for the customers.