ABSTRACT

Whenever mining association rules work for large data sets frequently itemset always play an important role and enhance the performance. Apriori algorithm is widely used for mining association rule which uses frequent item set but its performance can be improved by enhancing the performance of frequent itemsets. This paper proposes a new novel approach to finding frequent itemsets. The approach reduces a number of passes through an input data set in this paper from the study of data mining technology An Algorithm for Finding Frequent Itemset based on Lattice Approach for Lower Cardinality Dense and Sparse Dataset developed, 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. The paper presents the results of experiments conducted to find how performance of association rule mining algorithm depends on the values of parameters i.e. number of transaction, cardinality and minimum support..