Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems
The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item se...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Bali
2023-07-01
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| Series: | Matrix: Jurnal Manajemen Teknologi dan Informatika |
| Subjects: | |
| Online Access: | https://ojs2.pnb.ac.id/index.php/MATRIX/article/view/1059 |
| _version_ | 1797755904824705024 |
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| author | Moch. Syahrir Lalu Zazuli Azhar Mardedi |
| author_facet | Moch. Syahrir Lalu Zazuli Azhar Mardedi |
| author_sort | Moch. Syahrir |
| collection | DOAJ |
| description | The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets. |
| first_indexed | 2024-03-12T17:52:55Z |
| format | Article |
| id | doaj.art-c04e9da1a4134d5c9553c4222cd806f2 |
| institution | Directory Open Access Journal |
| issn | 2088-284X 2580-5630 |
| language | English |
| last_indexed | 2024-03-12T17:52:55Z |
| publishDate | 2023-07-01 |
| publisher | Politeknik Negeri Bali |
| record_format | Article |
| series | Matrix: Jurnal Manajemen Teknologi dan Informatika |
| spelling | doaj.art-c04e9da1a4134d5c9553c4222cd806f22023-08-03T00:16:14ZengPoliteknik Negeri BaliMatrix: Jurnal Manajemen Teknologi dan Informatika2088-284X2580-56302023-07-01132526710.31940/matrix.v13i2.52-67Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systemsMoch. Syahrir0Lalu Zazuli Azhar Mardedi1Universitas Bumigora, IndonesiaUniversitas Bumigora, IndonesiaThe popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.https://ojs2.pnb.ac.id/index.php/MATRIX/article/view/1059association rulefp-growthaprioritpq-apriorirapidminer |
| spellingShingle | Moch. Syahrir Lalu Zazuli Azhar Mardedi Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems Matrix: Jurnal Manajemen Teknologi dan Informatika association rule fp-growth apriori tpq-apriori rapidminer |
| title | Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems |
| title_full | Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems |
| title_fullStr | Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems |
| title_full_unstemmed | Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems |
| title_short | Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems |
| title_sort | determination of the best rule based analysis results from the comparison of the fp growth apriori and tpq apriori algorithms for recommendation systems |
| topic | association rule fp-growth apriori tpq-apriori rapidminer |
| url | https://ojs2.pnb.ac.id/index.php/MATRIX/article/view/1059 |
| work_keys_str_mv | AT mochsyahrir determinationofthebestrulebasedanalysisresultsfromthecomparisonofthefpgrowthaprioriandtpqapriorialgorithmsforrecommendationsystems AT laluzazuliazharmardedi determinationofthebestrulebasedanalysisresultsfromthecomparisonofthefpgrowthaprioriandtpqapriorialgorithmsforrecommendationsystems |