MapReduce Frequent Itemsets for Mining Association Rules

Frequent Patterns generation takes much time to execute, compute and the efficiency is less with massive datasets. In this paper, the enhanced FP-Growth (EFP-Growth) based on MapReduce framework using Hadoop approach has been proposed. The new method has high achieving performance with different minimum support. The EFP-growth can works with the large amount of datasets to discover a frequent patterns in a transaction database. With EFP-Growth, many challenges has been solved, to mine frequent patterns such as large memory consumption, high I/O cost, high time and space complexity. In the proposed method, the execution time under different minimum supports and large datasets is decreased.