An Enhanced Approach to Mine Maximal Frequent Itemset using Maximal Frequent Itemset Prima Algorithm (MFIPA)

Abstract

In data mining finding out the frequent itemsets is one of the very essential topics. Data mining helps in identifying the best knowledge for different decision makers. Frequent itemset generation is the precondition and most time-consuming method for association rule mining. In this paper we suggest a new algorithm for frequent itemset detection that works with datasets in distributed manner. The proposed algorithm brings in a new method to find frequent itemset not including the necessitate to create candidate itemsets. The proposed approach could be implemented using horizontal representation for transaction datasets and allocating prime value. It explores all the frequent itemset that is present in the input and according to the support the maximum frequent itemset is identified. It was applied on different transactions database and compared with well-known algorithms: FP-Growth and Parallel Apriori with different support levels. The try out showed that the proposed algorithm attain major time improvement over both algorithms.