Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor
© 2017 A major task in spatio-temporal outlier detection is to identify objects that exhibit abnormal behavior either spatially, and/or temporally. There have only been a few algorithms proposed for detecting spatial and/or temporal outliers. One example is the Local Density-Based Spatial Clustering of Applications with Noise (LDBSCAN). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is mainly for clustering; it just tells us whether an object belongs to a cluster or it is an outlier. A measure known as Local Outlier Factor (LOF) gives a quantitative measure of outlierness to each object, where a high LOF score means it is potentially an outlier. LDBSCAN algorithm, which combines the above notions, considers only the spatial context. Furthermore, the notion of a cluster is defeated (i.e. LDBSCAN may report clusters having less than the minimum required points in a cluster), and some of the outliers may not be detected because of the limitation of the existing conditions in the LDBSCAN algorithm. In this paper, we propose two algorithms, namely Spatio-Temporal Behavioral Density-based Clustering of Applications with Noise (ST-BDBCAN) and Approx-ST-BDBCAN. ST-BDBCAN algorithm adopts the proposed, new concept, called Spatio-Temporal Behavioral Outlier Factor (ST-BOF), which is a spatio-temporal extension to LOF. It also uses both spatial and temporal attributes simultaneously to define the context. By doing so, the relative importance of spatial continuity or temporal continuity appropriate to the application at hand can be established. The Approx-ST-BDBCAN algorithm achieves improved scalability, with minimal loss of detection accuracy by partitioning data points for parallel processing. Experimental results on synthetic, and buoy datasets suggest that our proposed algorithms are accurate and computationally efficient. Additionally, new Outlier Association with Hurricane Intensity Index (OAHII) measures are introduced for quantitative evaluation of the results from buoy dataset.
Data and Knowledge Engineering
Duggimpudi, M., Abbady, S., Chen, J., & Raghavan, V. (2019). Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor. Data and Knowledge Engineering. Retrieved from https://ir.una.edu/geography_facpub/30