Recommendation Using Frequent Itemset Mining in Big Data

Abstract

Recommendations helps to analyze and find customer behaviour more easily and helps to discover the relationship between unrelated data. It plays a vital role in Market basket Analysis. Recommendations using Frequent Itemset mining Algorithm help to identify more frequently occurring patterns and recommending the relationship or correlations between the items. By providing such Recommendations to the shopkeepers, helps them to increase their profits. As the data size keeps on increasing traditional methods could not handle the bulk amount of Data, so researchers have used the map reduce framework to handle huge volume of Data. In this method Rules are recommended using hybrid algorithm of Kmeans, Apriori and Eclat in Map reduce Framework.