Improved filtration step for mining association rules

In the course of association rules mining, frequent itemsets need to be identified. Well known association rule mining algorithm, Apriori, generates frequent itemsets. It generates frequent itemsets by repeating candidate generation and verification process until large itemsets are generated. Candidate generation process includes two steps-joining and pruning. An alternate for pruning step does the same job efficiently and is known as filtration. In this paper, an improved filtration step is proposed and evaluated for five standard databases. It is observed that improved filtration step is more efficient than pruning and filtration steps.