1. An Enhanced Frequent Pattern Growth Based on MapReduce for Mining Association Rules
- Author
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Yahya E. A. Al-Salhi, Arkan A. G Al-Hamodi, and Songfeng Lu
- Subjects
Association rule learning ,Computer science ,media_common.quotation_subject ,InformationSystems_DATABASEMANAGEMENT ,02 engineering and technology ,computer.software_genre ,Execution time ,GSP Algorithm ,020204 information systems ,Map reduce ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Quality (business) ,Data mining ,Database transaction ,computer ,media_common - Abstract
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..
- Published
- 2016