The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Findout: finding outliers in very large datasets
Knowledge and Information Systems
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
High dimensional reverse nearest neighbor queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Capabilities of outlier detection schemes in large datasets, framework and methodologies
Knowledge and Information Systems
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
International Journal of Intelligent Systems Technologies and Applications
Detection of moving objects using incremental connectivity outlier factor algorithm
Proceedings of the 47th Annual Southeast Regional Conference
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Outlier detection has recently become an important problem in many industrial and financial applications. Often, outliers have to be detected from data streams that continuously arrive from data sources. Incremental outlier detection algorithms, aimed at detecting outliers as soon as they appear in a database, have recently become emerging research field. In this paper, we develop an incremental version of connectivity-based outlier factor (COF) algorithm and discuss its computational complexity. The proposed incremental COF algorithm has equivalent detection performance as the iterated static COF algorithm (applied after insertion of each data record), with significant reduction in computational time. The paper provides theoretical and experimental evidence that the number of updates per such insertion/deletion does not depend on the total number of points in the data set, which makes algorithm viable for very large dynamic datasets. Finally, we also illustrate an application of the proposed algorithm on motion detection in video surveillance applications.