Abstract
The problem of anomaly detection in marine Argo data is studied. Based on the common and widely used DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm for anomaly detection in Argo data, as in other fields, there is a major problem in the application of DBSCAN algorithm, which is how to choose the appropriate parameter pairs. To solve this problem, this paper proposes an improved version of DBSCAN, namely CCMD-DBSCAN (DBSCAN based on the classification characteristics of marine data), which solves the above problem by studying the characteristics and laws of Argo data and is successfully applied to anomaly detection of marine data. The experimental results show that the new algorithm can not only determine the appropriate parameter pairs but also has a good anomaly detection effect.
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Jiang, Y., Kang, C., Shen, Y., Huang, T., Zhai, G. (2022). Research on Argo Data Anomaly Detection Based on Improved DBSCAN Algorithm. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_4
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DOI: https://doi.org/10.1007/978-981-19-8350-4_4
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