Implementing FP-Growth Algorithm using Map Reduce for Mining Association Rules

Abstract

Abstract: In mining frequent itemsets, one of most important algorithms is FP-growth. FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. Map Reduce is a distributed processing framework where the application is divided into many fragments of work, each of which may be executed on any node on a cluster. The main objective of this paper is Parallel FP-growth algorithm to achieve the quality of FP-growth. Our proposed method implemented the Parallel FP-Growth based on Map Reduce framework using Hadoop approach. New method has high achieving performance compared with the basic FP-Growth. The Parallel FP-growth algorithm can work with the large datasets to discovery frequent patterns in a transaction database. Based on our method, the execution time under different minimum supports is decreased.

Keywords: itemsets, FP-tree, hadoop, map reduce, support

Cite this Article

K. Purushotam Naidu, Ch. V.V.D. Prasad. Implementing FP-Growth Algorithm using MapReduce for Mining Association Rules. Journal of Advanced Database Management & Systems. 2019; 6(2):
18–29p.