An adaptive method for mining frequent itemsets efficiently: An improved header tree method

Data mining has become an important field and has been applied extensively across many different areas. Mining frequent itemsets from a transaction database is crucial for mining association rules. FP-growth algorithm has been widely used for frequent pattern mining and it is one of the most important algorithm proposed to efficiently mine association rules because it can dramatically improve the performance compared to the Apriori algorithm. Many investigations have proved that FP-growth method outperforms the method of Apriori-like candidate generation. The performance of the FP-growth method depends on many factors; the data structures, recursive creation of pattern trees, searching, sorting, insertion and many more. In all of the algorithms which are using fp-tree, a header table is used with sorted items. Header table is an important data structure in the mining process. The main datastructure (frequent trees) is created with the use of the header table. In this paper we suggest a new Binary Search Header Three (BSHT) and an Improved Header Tree mining (IHT-growth) to improve the performance of the frequent pattern mining. Experimental results show that the mining with BSHT is efficient for frequent pattern mining.