A Framework for Local Outlier Detection from Spatio-Temporal Trajectory Datasets

As one of the primary tasks in data mining, outlier detection serves a significant role in data quality enhancement for the scientific model prediction and revealing the abnormal hidden patterns from large scale trajectory datasets. In this paper, we introduce a versatile framework for detecting local trajectory outliers using spatial and temporal features of moving objects. Our local outlier detection consists of three phases. First, we divide the raw trajectory into trajectory segments by using a time-based partition strategy and extracting trajectory features from spatial attributes for each trajectory segment. Second, we create template trajectory segments based on a clustering schema. Finally, we compute the abnormal score, which measures the dissimilarity among the query and template trajectory segments, and thus determine the outlying trajectory segments according to the overall distribution of the abnormal score. To show the effectiveness of our approach, we conduct two case studies on the real-life solar active region and Coronal Mass Ejection (CME) trajectory datasets. Our results show that our local outlier detection method can successfully detect the reporting errors and anomalous phenomenon in both of our case studies.