Computational geometry: an introduction
Computational geometry: an introduction
ACM Computing Surveys (CSUR)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd 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 Replacement for Voronoi Diagrams of Near Linear Size
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
SLOM: a new measure for local spatial outliers
Knowledge and Information Systems
Finding centric local outliers in categorical/numerical spaces
Knowledge and Information Systems
An Efficient Reference-Based Approach to Outlier Detection in Large Datasets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Outlier Detection with Kernel Density Functions
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
An efficient histogram method for outlier detection
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
A nonparametric outlier detection for effectively discovering top-n outliers from engineering data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Ranking outliers using symmetric neighborhood relationship
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Outlier mining is an important branch of data mining and has attracted much attention recently. The density-based method LOF is widely used in application. However, selecting MinPtsis non-trivial, and LOF is very sensitive to its parameters MinPts. In this paper, we propose a new outlier detection method based on Voronoi diagram, which we called Voronoi based Outlier Detection (VOD). The proposed method measures the outlier factor automatically by Voronoi neighborhoods without parameter, which provides highly-accurate outlier detection and reduces the time complexity from O(n2) to O(nlogn).