Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques
Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of t...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/10/5498 |
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| author | Ioan Daniel Hunyadi Nicolae Constantinescu Oana-Adriana Țicleanu |
| author_facet | Ioan Daniel Hunyadi Nicolae Constantinescu Oana-Adriana Țicleanu |
| author_sort | Ioan Daniel Hunyadi |
| collection | DOAJ |
| description | Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints. |
| format | Article |
| id | doaj-art-9b7bc40b148c4d25a6499b6ba8a9934b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9b7bc40b148c4d25a6499b6ba8a9934b2025-08-20T03:47:49ZengMDPI AGApplied Sciences2076-34172025-05-011510549810.3390/app15105498Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth TechniquesIoan Daniel Hunyadi0Nicolae Constantinescu1Oana-Adriana Țicleanu2Faculty of Science, Department of Mathematics and Informatics, Lucian Blaga University of Sibiu, 550012 Sibiu, RomaniaFaculty of Science, Department of Mathematics and Informatics, Lucian Blaga University of Sibiu, 550012 Sibiu, RomaniaFaculty of Science, Department of Mathematics and Informatics, Lucian Blaga University of Sibiu, 550012 Sibiu, RomaniaAssociation rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints.https://www.mdpi.com/2076-3417/15/10/5498association rule miningApriori algorithmFP-Growth algorithme-commerce data miningfrequent itemsetscomparative analysis |
| spellingShingle | Ioan Daniel Hunyadi Nicolae Constantinescu Oana-Adriana Țicleanu Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques Applied Sciences association rule mining Apriori algorithm FP-Growth algorithm e-commerce data mining frequent itemsets comparative analysis |
| title | Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques |
| title_full | Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques |
| title_fullStr | Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques |
| title_full_unstemmed | Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques |
| title_short | Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques |
| title_sort | efficient discovery of association rules in e commerce comparing candidate generation and pattern growth techniques |
| topic | association rule mining Apriori algorithm FP-Growth algorithm e-commerce data mining frequent itemsets comparative analysis |
| url | https://www.mdpi.com/2076-3417/15/10/5498 |
| work_keys_str_mv | AT ioandanielhunyadi efficientdiscoveryofassociationrulesinecommercecomparingcandidategenerationandpatterngrowthtechniques AT nicolaeconstantinescu efficientdiscoveryofassociationrulesinecommercecomparingcandidategenerationandpatterngrowthtechniques AT oanaadrianaticleanu efficientdiscoveryofassociationrulesinecommercecomparingcandidategenerationandpatterngrowthtechniques |