PERFORMANCE ANALYSIS OF ASSOCIATION RULE MINING ALGORITHMS USING HADOOP

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Yevrosiyo matematik nazariya va kompyuter fanlari jurnali
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Issue: Vol. 2 No. 14 : Eurasian Journal of Mathematical Theory and Computer Sciences
Section: Articles
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Assistant of the Department of “Software Engineering”, Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Kharezmi, Khorezm, Uzbekistan. “ 05.00.00 - Texnika fanlari, Axborot texnologiyalari sohalari”
info@in-academy.uz

Abstract:

Association rule mining has been a very important method in the field of data mining. Apriori algorithm is a classical algorithm for association rule mining. In the big data environment, the traditional Apriori algorithm has been unable to meet the needs of mining. In the paper, the parallelization of the Apriori algorithm is implemented based on the Hadoop platform and the Map Reduce programming model. On the basis, the algorithm is further optimized by using the idea of transaction reduction. Experimental results show that the proposed algorithm can be better to meet the requirements of big data mining and efficiently mining frequent itemsets and association rules from large dataset.

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How to Cite:

Rakhimova , L. (2022). PERFORMANCE ANALYSIS OF ASSOCIATION RULE MINING ALGORITHMS USING HADOOP. Eurasian Journal of Mathematical Theory and Computer Sciences, 2(14), 43–47. Retrieved from https://in-academy.uz/index.php/EJMTCS/article/view/7761

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