Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology
With the continuous improvement of digitization, the processing and analysis of massive data has become one of the hot issues. Soft computing technology, as an emerging machine intelligence technology, performs well in handling complex uncertainty problems and is an important component of artificial...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10384345/ |
| _version_ | 1797243411181338624 |
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| author | Chunhua Liang |
| author_facet | Chunhua Liang |
| author_sort | Chunhua Liang |
| collection | DOAJ |
| description | With the continuous improvement of digitization, the processing and analysis of massive data has become one of the hot issues. Soft computing technology, as an emerging machine intelligence technology, performs well in handling complex uncertainty problems and is an important component of artificial intelligence. This study takes soft computing technology as the technical core and constructs a fuzzy dynamic clustering model based on improved immune algorithms to process unlabeled data. And an anomaly detection and analysis algorithm is designed based on soft instance transfer learning to handle weakly labeled data. The performance test outcomes denote that the accuracy, recall, and F1 values of the immune optimization fuzzy dynamic clustering algorithm are 91.69%, 89.27%, and 92.15%, respectively, reaching the optimal level of similar intelligent optimization clustering algorithms. The immune optimization fuzzy dynamic clustering algorithm has better computational efficiency, loss function curve performance, and strong global search ability, and avoids the occurrence of local optimal solutions. Compared with other advanced clustering algorithms, the immune optimization fuzzy dynamic clustering algorithm performs well on datasets in various fields, and both external and internal evaluation indicators verify the algorithm’s clustering effect. The AUC value of the soft computing instance transfer learning anomaly detection algorithm is 0.913, with a detection accuracy of 91.67%, which is superior to other anomaly detection algorithms. The unlabeled and weak-label data processing model designed based on soft computing technology can effectively achieve the processing and analysis of real-world data problems. |
| first_indexed | 2024-04-24T18:54:41Z |
| format | Article |
| id | doaj.art-056a061870d045619395fcfc5e986b13 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:41Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-056a061870d045619395fcfc5e986b132024-03-26T17:34:59ZengIEEEIEEE Access2169-35362024-01-01126852686310.1109/ACCESS.2024.335136510384345Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing TechnologyChunhua Liang0https://orcid.org/0009-0008-4119-6691Institute for History of Science and Technology, Shanxi University, Taiyuan, ChinaWith the continuous improvement of digitization, the processing and analysis of massive data has become one of the hot issues. Soft computing technology, as an emerging machine intelligence technology, performs well in handling complex uncertainty problems and is an important component of artificial intelligence. This study takes soft computing technology as the technical core and constructs a fuzzy dynamic clustering model based on improved immune algorithms to process unlabeled data. And an anomaly detection and analysis algorithm is designed based on soft instance transfer learning to handle weakly labeled data. The performance test outcomes denote that the accuracy, recall, and F1 values of the immune optimization fuzzy dynamic clustering algorithm are 91.69%, 89.27%, and 92.15%, respectively, reaching the optimal level of similar intelligent optimization clustering algorithms. The immune optimization fuzzy dynamic clustering algorithm has better computational efficiency, loss function curve performance, and strong global search ability, and avoids the occurrence of local optimal solutions. Compared with other advanced clustering algorithms, the immune optimization fuzzy dynamic clustering algorithm performs well on datasets in various fields, and both external and internal evaluation indicators verify the algorithm’s clustering effect. The AUC value of the soft computing instance transfer learning anomaly detection algorithm is 0.913, with a detection accuracy of 91.67%, which is superior to other anomaly detection algorithms. The unlabeled and weak-label data processing model designed based on soft computing technology can effectively achieve the processing and analysis of real-world data problems.https://ieeexplore.ieee.org/document/10384345/Soft computinglabeled datacluster analysisweak-labelunlabeled |
| spellingShingle | Chunhua Liang Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology IEEE Access Soft computing labeled data cluster analysis weak-label unlabeled |
| title | Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology |
| title_full | Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology |
| title_fullStr | Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology |
| title_full_unstemmed | Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology |
| title_short | Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology |
| title_sort | clustering analysis of unlabeled data and weak label detection analysis method integrating soft computing technology |
| topic | Soft computing labeled data cluster analysis weak-label unlabeled |
| url | https://ieeexplore.ieee.org/document/10384345/ |
| work_keys_str_mv | AT chunhualiang clusteringanalysisofunlabeleddataandweaklabeldetectionanalysismethodintegratingsoftcomputingtechnology |