A Data Mining Algorithm for Association Rules with Chronic Disease Constraints
The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China’s medical field of association between chronic diseases has some problems, such as the need to scan the transactio...
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| Published in | Computational intelligence and neuroscience Vol. 2022; pp. 1 - 8 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Hindawi
23.08.2022
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2022/8526256 |
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| Abstract | The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China’s medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved. |
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| AbstractList | The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China’s medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved. The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China's medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved.The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China's medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved. |
| Audience | Academic |
| Author | Liu, YanRong Miao, Rong Wang, LiJun Ren, HengNi |
| AuthorAffiliation | College of Information Engineering, Shaanxi Institute of International Trade & Commerce, Xi'an 712046, China |
| AuthorAffiliation_xml | – name: College of Information Engineering, Shaanxi Institute of International Trade & Commerce, Xi'an 712046, China |
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| Cites_doi | 10.7334/psicothema2021.62 10.1016/j.imu.2020.100494 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 YanRong Liu et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 YanRong Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 YanRong Liu et al. 2022 |
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| References | K. Kari (2) 2022; 2 N. N. Zhang (8) 2022; 43 P. P Wang (13) 2019; 36 T. T. Chen (14) 2019; 40 W. T. Liu (16) 2020; 40 Z. H. Chen (4) 2018; 40 F. Wen (10) 2020; 34 Y. R. Liu (17) 2021; 31 L. Miao (5) 2021; 35 X. B. Xu (6) 2021; 29 K. Zhou (9) 2018; 34 J. J. Yan (1) 2020; 56 Q. Guo (11) 2021; 42 7 W. L. Ji (15) 2020; 56 J. Li (3) 2021; 42 Q. Zhu (18) 2020; 43 X. Li (19) 20 X. B. Sun (12) 2018; 39 |
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| SubjectTerms | Algorithms Care and treatment Chronic diseases Chronic illnesses Classification Clustering Data mining Diabetes Efficiency Electronic commerce Mathematical optimization Matrix methods Medical research Neural networks Optimization |
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| Title | A Data Mining Algorithm for Association Rules with Chronic Disease Constraints |
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