Evaluating the effectiveness of association rules mining algorithms in parallel processing environment
Corressponding author's email:
sonnt@hcmute.edu.vnKeywords:
Data Mining, Association Rule Mining, Apriori, FP-Growth, Improved AprioryAbstract
An association rule indicates the relationship, association, or correlation between objects in the database. Association Rule Mining is a problem which has received an increasing amount of attention lately in data mining. Appriori and FP-Growth have commonly used algorithms in association rule mining. The aim of the paper is to evaluate the effectiveness of the Apriori, FP_Growth and Improved Apriori in a parallel processing environment. The comparison is based on execution time and the performance. The experimental results showed that in the parallel processing environment the FP-growth algorithm is the most efficient one.
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