COMPARATIVE STUDY OF FREQUENT ITEMSET MINING ALGORITHMS APRIRIO AND FP GROWTH

In this paper generating frequent itemsets are discussed: Apriori and FP-growth algorithm. In apriori algorithm candidates are generated and testing is done which is easy to implement but candidate generation and support counting is very expensive in this because database is checked many times. In the fp-growth, there is no candidate generation and requires only 2 passes over the database but in this the generation of fp-tree become very expansive to built and support is counted only when entire dataset is added to fptree. The comparison of these algorithms are present as in this paper which shows better performance.