Frequent Itemset Mining techniques — A technical review

Abstract

Frequent Itemset Mining is one of the most popular techniques to extract knowledge from data. However, these mining methods become more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming provide many tools to tackle this problem. However, these tools come with their own technical challenges such as balanced data distribution and inter-communication costs. In this paper, we are presenting a detailed survey of Hadoop, which helps in storing data and parallel processing in distributed environment. Here we have explored various Frequent Itemset Mining techniques on parallel and distributed environment. The aim of this paper is to present a comparison of different frequent itemset mining techniques and help to develop efficient and scalable frequent itemset mining techniques.