An Algorithm for Finding Frequent Itemset based on Lattice Approach for Lower Cardinality Dataset

Abstract

In recent years, mining for association rules between items sets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. However, the previously proposed methods still encounter some performance bottlenecks. We have developed An Algorithm for Finding Frequent Itemset based on Lattice Approach for Lower Cardinality Dataset, by making variation in Apriori, which improves performance over Apriori for lower cardinality. It does not follow generation of candidate-and-test method. It also reduces the scanning of database and needs only two scanning of database.