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Research and Improvement for Apriori Algorithm of Association Rule Mining

Xizhao Luo

Open publisher page 3 citations

Abstract

On the basis of deep research and analysis of Apriori algorithm in association rule mining,the paper discovers some shortages of the algorithm,and then improves it from three aspects: Firstly,the calculation method of support in candidate frequent itemsets is improved;Secondly,the scale of candidate frequent itemsets is reduced;In the end,the numbers of scanned database are decreased.The experiment results of the improved algorithm show that the improved algorithm is more efficient than the original.

About this research paper

What this paper is about

On the basis of deep research and analysis of Apriori algorithm in association rule mining,the paper discovers some shortages of the algorithm,and then improves it from three aspects: Firstly,the calculation method of support in candidate frequent itemsets is improved;Secondly,the scale of candidate frequent itemsets is reduced;In the end,the numbers of scanned database are decreased.The experiment results of the improved algorithm show that the improved algorithm is more efficient than the original.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

On the basis of deep research and analysis of Apriori algorithm in association rule mining,the paper discovers some shortages of the algorithm,and then improves it from three aspects: Firstly,the calculation method of support in candidate frequent itemsets is improved;Secondly,the scale of candidate frequent itemsets is reduced;In the end,the numbers of scanned database are decreased.The experiment results of the improved algorithm show that the improved algorithm is more efficient than the original.

Key concepts: Association rule learning, Apriori algorithm, Computer science, Economic shortage, Data mining, A priori and a posteriori, GSP Algorithm, Algorithm

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