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In mining frequent itemsets, one of most important algorithm 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. In this paper, we propose the EFP-growth (enhanced FPgrowth) algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth based on MapReduce framework using Hadoop approach. New method has high achieving performance compared with the basic FP-Growth. The EFP-growth it 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..

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.

In this paper, a novelty of a new method has been presented. MRFP-Growth is implementing to solve the problem of discovering frequent patterns with massive datasets. Most of the frequent patterns algorithms are unable to manipulate with the large datasets, our scheme implement FP-Growth method under MapReduce framework in Hadoop platform to decrease the massive datasets. The efficiency and performance of a new method has been increased compare with other mining algorithms.