Efficient Pattern Mining Using Integrated Range of Item Sets

Abstract

Association rule mining is one of the well-known data mining techniques to identify user interest and their behavioural pattern.  It's known as market basket analysis used in all kinds of business-oriented fields to understand customer's interest. The Apriori is used to find frequent by analysing transaction database and to generate association rules. The existing Apriori algorithm suffers with usage of more storage and execution time as it needs to scan the main data base frequently each time while generation of candidate item sets. The proposed algorithm reduces time complexity, input-output load, and memory utilisation by eliminating some of the duplicate combinations which reduces number of scans required to achieve accurate and reliable results with high efficiency.