A Comparison of Apriori and FP-Growth Algorithms with Groceries dataset การเปรียบเทียบประสิทธิภาพอัลกอริทึม Apriori และ FP-Growth ด้วยชุดข้อมูลร้านขายของชำ
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ABSTRACT
This research aims to 1) study and compare the performance of the Apriori and FP-Growth algorithms in finding frequent itemsets, which is a crucial step in the process of association rules mining, and 2) compare the speed and memory usage of the algorithms in finding frequent itemsets.
The research findings indicate that 1) each algorithm used for discovering frequent itemsets has different advantages and disadvantages depending on there dataset and support threshold used. And 2) experimental comparison of algorithm speeds and memory usage in finding frequent itemsets reveals that the FP-Growth algorithm performs faster than the Apriori algorithm and utilizes less memory. This is because the Apriori algorithm requires multiple scans of the database and generates candidate itemsets, leading to longer processing times. On the other hand, the FP-Growth algorithm scans the database only twice and does not generate candidate itemsets, resulting in faster execution.
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References
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