Parallel Processing of Frequent Itemset Based on MapReduce Programming Model

Searching frequent itemset in large size diverse database is one of the most important data mining problem and as existing algorithms are insufficient in mechanism that enables automatic parallelization, fault tolerance and data distribution. Solution to this issue we design algorithm using MapReduce programming model. The overarching aim is to enhance the performance of parallel frequent itemset mining on Hadoop. Incorporating ultra-metric tress to improve more efficiency of mining frequent itemset and comparing Apriori algorithm and FP-Growth algorithm based on some parameters. We implement the algorithm with dataset of Market Basket Analytics