COMPARING THE PERFORMANCE BETWEEN FP-GROWTH AND APRIORI ALGORITHMS FOR ANALYZING SHOPPING PATTERNS IN A COFFEE SHOP
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Abstract
The current study, namely Comparison of FP-Growth and Apriori Algorithms for Analyzing Shopping Patterns in a Coffee Shop, was aimed to investigate association rules and compare the performance of the Apriori and FP-Growth algorithms in analyzing customer purchase transactions in a coffee shop. The dataset, consisting of 1,000 transactions, was obtained from the Kaggle website then analyzed using data mining techniques with the Python programming language and the mlxtend library. The research results were revealed that both algorithms could generate association rules with high confidence values. For example, the rule (Latte) → (Croissant) achieved a confidence of 85%. However, FP-Growth algorithms demonstrated the better performance than Apriori’s, spending 0.01 seconds of processing time and using 30.34 KB of memory. On the other hand, Apriori algorithms spending 0.00 seconds and using 30.33 KB. Although Apriori algorithms are suitable for small datasets, FP-Growth offers a structural advantage through its use of the FP-Tree, which reduces redundant data scans. FP-Growth algorithms are more suitable for large and complex datasets.
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