An efficient method for discovery of large item sets

In today's emerging field of descriptive data mining, association rule mining (ARM) has been proven helpful to describe essential characteristics of data from large databases. Mining frequent item sets is the fundamental task of ARM. Apriori, the most influential traditional ARM algorithm, adopts iterative search strategy for frequent item set generation. But, multiple scans of database, candidate item set generation and large load of system's I/O are major abuses which degrade the mining performance of it. Therefore, we proposed a new method for mining frequent item sets which overcomes these shortcomings. It judges the importance of occurrence of an item set by counting present and absent count of an individual item. Performance evaluation with Apriori algorithm shows that proposed method is more efficient as it finds fewer items in frequent item set in 50% less time without backtracking. It also reduces system I/O load by scanning the database only once.