Frequent Itemset Generation using Double Hashing Technique

In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. In this paper, we propose a new association rule mining algorithm called Double Hashing Based Frequent Itemsets, (DHBFI) in which hashing technology is used to store the database in vertical data format. This double hashing technique is mainly preferred for avoiding the major issues of hash collision and secondary clustering problem in frequent itemset generation. Hence this proposed hashing technique makes the computation easier, faster and more efficient. Also this algorithm eliminates unnecessary redundant scans in the database and candidate itemset generation which leads to less space and time complexity. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education