1. MapReduce Frequent Itemsets for Mining Association Rules
- Author
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Arkan A. G. Al-Hamodi and Song-Feng Lu
- Subjects
Consumption (economics) ,0209 industrial biotechnology ,Association rule learning ,Computer science ,02 engineering and technology ,computer.software_genre ,Execution time ,Statistical classification ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Algorithm design ,Data mining ,Database transaction ,computer - Abstract
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.
- Published
- 2016