Spatial-temporal trajectory anomaly detection based on an improved spectral clustering algorithm

• Published in: Intelligent data analysis 2023-01, Vol.27 (1), p.31-58
• Main Authors: Guo, Yishan, Liu, Mandan
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Clustering; Eigenvalues; Eigenvectors; Spatial data; Wireless communications; Wireless networks
• ISSN: 1088-467X; 1571-4128
• DOI: 10.3233/IDA-216185

With the development of wireless communication technology, when users use wireless networks to meet various needs, wireless networks also record a large number of users’ spatial-temporal trajectory data. In order to better pay attention to the healthy development of students and promote the information construction on campus, a spectral clustering algorithm based on the multi-scale threshold and density combined with shared nearest neighbors (MSTDSNN-SC) is proposed. Firstly, it improves the affinity distance function based on the shortest time dis-tance-shortest time distance sub-sequence (STD-STDSS) by adding location popularity and uses this model to construct the initial adjacency matrix. Then it introduces the covariance scale threshold and spatial scale threshold to perform 0–1 processing on the adjacency matrix to obtain more accurate sample similarity. Next, it constructs an eigenvector space by eigenvalue decom-position of the adjacency matrix. Finally, it uses DBSCAN clustering algorithm with shared nearest neighbors to avoid to manually determine the number of clusters. Taking Internet usage data on campus as an example, multiple clustering algorithms are used for anomaly detection and four evaluation metrics are applied to estimate the clustering results. MSTDSNN-SC algorithm reflects better clustering performance. Furthermore, the abnormal trajectories list is verified to be effective and credible.