Robust regression and outlier detection
Robust regression and outlier detection
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
BACON: blocked adaptive computationally efficient outlier nominators
Computational Statistics & Data Analysis
Robust space transformations for distance-based operations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Approximation Scheme for Data Mining Tasks
Proceedings of the 17th International Conference on Data Engineering
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
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A novel approach to outlier detection on the ground of the properties of distribution of distances between multidimensional points is presented. The basic idea is to evaluate the outlier factor for each data point. The factor is used to rank the dataset objects regarding their degree of being an outlier. Selecting the points with the minimal factor values can then identify outliers. The main advantages of the approach are: (1) no parameter choice in outlier detection is necessary; (2) detection is not dependent on clustering algorithms. To demonstrate the quality of the outlier detection, the experiments were performed on widely used datasets. A comparison with some popular detection methods shows the superiority of our approach.