LOF: identifying density-based local outliers
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
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Incremental Clustering of Mobile Objects
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
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As the collection of moving object data become much easier, event-based outlier detection such as congestion in trajectory data are becoming increasingly attractive to data mining community. Most of the existing methods only perform the trajectory outlier detection on the spatial information. In this pape, a framework for congestion outlier detection with clustering method was proposed. Trajectory data are analyzed according, to both temporal and spatial factors by introducing the concept of minimal bounding boxes (MBBs), and superdense clusters are regarded as congestion outliers. Experiments show the capability and efficiency of the proposed approach.