A Hash Based Frequent Item set Mining using Rehashing

Abstract

Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items. Mining frequent item sets is one of the most important concepts of data mining. Frequent item set mining has been a highly concerned field of data mining for researcher for over two decades. It plays an essential role in many data mining tasks that try to find interesting itemsets from databases, such as association rules, correlations, sequences, classifiers and clusters . In this paper, we propose a new association rule mining algorithm called Rehashing Based Frequent Item set (RBFI) in which hashing technology is used to store the database in vertical data format. To avoid hash collision and secondary clustering problem in hashing, rehashing technique is utilized here. The advantages of this new hashing technique are easy to compute the hash function, fast access of data and efficiency. This algorithm provides facilities to avoid unnecessary scans to the database.