篇名 |
Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering
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並列篇名 | Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering |
作者 | Lixia Ji、Xiao Zhang、Yao Zhao、Zongkun Li |
英文摘要 | In response to the abnormal data mining in dam safety monitoring, and based on the traditional spectral clustering, this paper presents an anomaly detection method based on improved spectral clustering. This method applies a distance and density adaptive similarity measure. The natural eigenvalue is introduced to adaptively select the neighbors of data points, and the similarity is redefined to be combined with the natural k-nearest neighbor. Furthermore, the shared neighbor is introduced to adjust the similarity between the monitoring data samples according to the regional density. Moreover, considering the distribution of dam monitoring data, the initialization of clustering centers is optimized according to both the density and distance feature. This method can prevent the algorithm from local optimum, better adapt to the density of non-convex dataset, reduce the number of iterations, and enhance the efficiencies of clustering and anomaly detection. Taking the dam slab monitoring data as the research object, experimental datasets are formed. Experiments on these datasets further verify that the method of this paper can effectively adapt to discrete distribution datasets and is superior to the classical spectral clustering method in both clustering and anomaly detection.
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起訖頁 | 749-759 |
關鍵詞 | Anomaly detection、Dam、Spectral clustering、Natural eigenvalue、Shared neighbor |
刊名 | 網際網路技術學刊 |
期數 | 202207 (23:4期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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