LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Compact Representation of Spatio-Temporal Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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The development of mobile device technology and localization technology makes the collection of spatio-temporal information from moving objects much easier than before, and outlier detection for spatio-temporal trajectory is becoming increasingly attractive to data mining community. However, there is a lack of serious studies in this area. Several existing trajectory outlier methods such as the partition-and-detect framework can only deal with the trajectory data which only includes spatial attributes. It cannot be applied to the spatio-temporal trajectory data which includes both spatial and temporal attributes. In this paper, we propose an enhanced partition-and-detect framework to detect the outliers of spatio-temporal trajectory data. In this framework, we mainly introduce an outlier detection method which uses trajectory MBBs(Minimum Boundary Boxs). Based on this enhanced framework, we propose a congestion outlier detection method. Finally, the efficiency and accuracy are evaluated through experiments which use a real traffic dataset called US Highway 101 Dataset.