Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm
Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/4969233 |
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| _version_ | 1832547657328885760 |
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| author | Yan Wu Yonghong Chen Wenhao Ling |
| author_facet | Yan Wu Yonghong Chen Wenhao Ling |
| author_sort | Yan Wu |
| collection | DOAJ |
| description | Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is still in the early stage of development. Many existing algorithms are very sensitive to clustering parameters, especially scale parameters, and need to manually input the number of clustering. Therefore, a density-sensitive similarity measure is introduced in this paper, which is obtained by introducing new parameters to transform the Gaussian function. Under this metric, the distance between data points belonging to different classes will be effectively amplified, while the distance between data points belonging to the same class will be reduced, and finally, the distribution of data will be effectively clustered. At the same time, the idea of Eigen gap is introduced into the spectral clustering algorithm, and the verified gap sequence is constructed on the basis of Laplace matrix, so as to solve the problem of the number of initial clustering. The strong global search ability of artificial bee colony algorithm is used to make up for the shortcoming of spectral clustering algorithm that is easy to fall into local optimal. The experimental results show that the adaptive spectral clustering algorithm can better identify the initial clustering center, perform more effective clustering, and detect abnormal behavior more accurately. |
| format | Article |
| id | doaj-art-9d88a5e3be0b4a69a4efca6cdd9de6d3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9d88a5e3be0b4a69a4efca6cdd9de6d32025-02-03T06:43:56ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/49692334969233Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering AlgorithmYan Wu0Yonghong Chen1Wenhao Ling2School of Business, Xi’an University of Finance and Economics, Shaanxi, Xi’an 710100, ChinaSchool of Public Administration, Xi’an University of Finance and Economics, Shaanxi, Xi’an 710061, ChinaInstitute of Local Government and Societal Governance, Henan University, Kaifeng, Henan 475004, ChinaAbnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is still in the early stage of development. Many existing algorithms are very sensitive to clustering parameters, especially scale parameters, and need to manually input the number of clustering. Therefore, a density-sensitive similarity measure is introduced in this paper, which is obtained by introducing new parameters to transform the Gaussian function. Under this metric, the distance between data points belonging to different classes will be effectively amplified, while the distance between data points belonging to the same class will be reduced, and finally, the distribution of data will be effectively clustered. At the same time, the idea of Eigen gap is introduced into the spectral clustering algorithm, and the verified gap sequence is constructed on the basis of Laplace matrix, so as to solve the problem of the number of initial clustering. The strong global search ability of artificial bee colony algorithm is used to make up for the shortcoming of spectral clustering algorithm that is easy to fall into local optimal. The experimental results show that the adaptive spectral clustering algorithm can better identify the initial clustering center, perform more effective clustering, and detect abnormal behavior more accurately.http://dx.doi.org/10.1155/2021/4969233 |
| spellingShingle | Yan Wu Yonghong Chen Wenhao Ling Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm Complexity |
| title | Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm |
| title_full | Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm |
| title_fullStr | Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm |
| title_full_unstemmed | Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm |
| title_short | Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm |
| title_sort | audit analysis of abnormal behavior of social security fund based on adaptive spectral clustering algorithm |
| url | http://dx.doi.org/10.1155/2021/4969233 |
| work_keys_str_mv | AT yanwu auditanalysisofabnormalbehaviorofsocialsecurityfundbasedonadaptivespectralclusteringalgorithm AT yonghongchen auditanalysisofabnormalbehaviorofsocialsecurityfundbasedonadaptivespectralclusteringalgorithm AT wenhaoling auditanalysisofabnormalbehaviorofsocialsecurityfundbasedonadaptivespectralclusteringalgorithm |