Interestingness measures for association rules: Combination between lattice and hash tables

There are many methods which have been developed for improving the time of mining frequent itemsets. However, the time for generating association rules were not put in deep research. In reality, if a database contains many frequent itemsets (from thousands up to millions), the time for generating association rules is more longer than the time for mining frequent itemsets. In this paper, we present a combination between lattice and hash tables for mining association rules with different interestingness measures. Our method includes two phases: (1) building frequent itemsets lattice and (2) generating interestingness association rules by combining between lattice and hash tables. To compute the measure value of a rule fast, we use the lattice to get the support of the left hand side and use hash tables to get the support of the right hand side. Experimental results show that the mining time of our method is more effective than the method that of directly mining from frequent itemsets uses hash tables only.