Comparative study of frequent itemset mining algorithms apriori and fp growth
International Journal of Development Research
Comparative study of frequent itemset mining algorithms apriori and fp growth
Received 19th February, 2018; Received in revised form 01st March, 2018; Accepted 28th April, 2018; Published online 31st May, 2018
Copyright © 2018, Banumathi and Dhurga Devi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this paper generating frequent item sets 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 fp tree. The comparison of these algorithms are present as in this paper which shows better performance.
