Mining frequent itemsets with diverse association rule mining: A survey

Frequent itemset mining leads to the discovery of association and correlation among items in large transactional or relational data sets. Massive amount of data continuously being collected and stored, many industries are becoming interested in mining such patterns from their databases. The main purpose of data mining provides superior result for using knowledge base system. Researchers presented a lot of approaches and algorithms for determining association rules. This paper discusses few approaches for mining association rules. Association rule mining approach is the most efficient data mining method to find out hidden or required pattern among the large volume of data. It is responsible to find correlation relationships among various data attributes in a huge set of items in a database. Apriori algorithm is an illustration of an enhanced association rule mining algorithm, which supports to avoid the replication of same items. Weighted Association Rule Mining (WARM) makes use of the importance of each itemset and transaction. WARM requires each item to be given weight to reflect their importance to the user. This paper also discusses several variations to the Apriori algorithm for improved efficiency and scalability. An improved frequent pattern growth adapts a divide and conquers strategy that compresses the database representing frequent items into a frequent pattern tree and compressed into a set of conditional databases.