Packaging Big Data Visualization Based on Computational Intelligence Information Design
A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the...
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| Vydané v: | Computational intelligence and neuroscience Ročník 2022; s. 1 - 10 |
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| Médium: | Journal Article |
| Jazyk: | English |
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United States
Hindawi
23.04.2022
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm. |
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| AbstractList | A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm. A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm.A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm. |
| Audience | Academic |
| Author | Zhang, Guangchao |
| AuthorAffiliation | College of Art and Design, Hainan University, Haikou 570228, Hainan, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35502359$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1587/elex.14.20170463 10.1016/j.knosys.2015.05.014 10.1111/jfpe.12647 10.1109/mc.2017.95 10.1016/j.compeleceng.2021.107136 10.1007/s12650-018-0481-7 10.1016/j.oceaneng.2021.108823 10.1158/1538-7445.am2017-2605 10.1109/mc.2015.332 10.1109/mis.2016.54 10.1016/j.cjche.2018.06.009 10.1109/mc.2019.2918513 10.1109/tvcg.2015.2484343 10.1016/j.neucom.2016.04.039 10.1109/mits.2015.2503200 10.1016/j.is.2014.07.006 10.1109/tsm.2019.2938157 |
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| Copyright | Copyright © 2022 Guangchao Zhang. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Guangchao Zhang. 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 Guangchao Zhang. 2022 |
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| SubjectTerms | Algorithms Artificial Intelligence Big Data Classification Communication Computer applications Customization Data Visualization Design Entropy Information sharing Information sources Information storage Information technology Intelligence Internet Online data bases Optimization Overcrowding Packaging Product life cycle Sampling Scientific visualization Society User needs Visualization Visualization (Computers) Weight reduction |
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