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Research and Improvement of Apriori Algorithm Based on Hadoop
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Abstract
Association rules can forcefully get a horizontal relation in the big data, the Apriori algorithm is one of the most significant association rules. Traditional mining based on parallel Apriori algorithms needs much more time in data IO with the increasing size of large transaction database. This paper improves the Apriori algorithm from compressing transactions, reducing the number of scans and simplifying candidate set generation. And then the improved algorithm is parallelized on the Hadoop framework. The experiments show that this improved algorithm is suitable for large-scale data mining and has good scalability and effectiveness.
Walter de Gruyter GmbH
Title: Research and Improvement of Apriori Algorithm Based on Hadoop
Description:
Abstract
Association rules can forcefully get a horizontal relation in the big data, the Apriori algorithm is one of the most significant association rules.
Traditional mining based on parallel Apriori algorithms needs much more time in data IO with the increasing size of large transaction database.
This paper improves the Apriori algorithm from compressing transactions, reducing the number of scans and simplifying candidate set generation.
And then the improved algorithm is parallelized on the Hadoop framework.
The experiments show that this improved algorithm is suitable for large-scale data mining and has good scalability and effectiveness.
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