A Comparative Analysis on Mining Frequent Itemsets

D.Kerana Hanirex


Research on mining frequent itemsets is one of ` the emerging task in data mining.The purchasing of one product when another product is purchased represents an association rule. Association rules are useful for analyzing the  customer behavior. It takes an important part in shopping basket data analysis, clustering. The FP-Growth and Apriori  algorithm is the basic algorithm for mining association rules. This paper presents an efficient algorithm for mining frequent itemsets using  Two Dimensional Transactions Reduction(TDTR)  approach which  reduces the original  database(D) transactions to the reduced data base transactions D1 based on the min_sup count. Then for each item it finds the number of transactions that the item present  and hence find  the largest frequent itemset using the  two dimensional approach. Using the largest item set property ,it finds the subset of frequent item sets. Thus  TDTR approach  reduces the number of scans in the database and hence improve the efficiency & accuracy by finding the number of association rules and reduces time to find the rules.This proposed approach compares the efficiency with traditional Apriori and FP-Growth algorithm.


Data mining,Association rule, Apriori Algorithm,FP-Growth algorithm, frequent Itemset, transaction reduction


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