| Package | Description | 
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.outlier.distance | 
 Distance-based outlier detection algorithms, such as DBOutlier and kNN. 
 | 
| Class and Description | 
|---|
| AbstractDBOutlier
 Simple distance based outlier detection algorithms. 
 | 
| AbstractDBOutlier.Parameterizer
 Parameterization class. 
 | 
| DBOutlierDetection
 Simple distanced based outlier detection algorithm. 
 | 
| DBOutlierScore
 Compute percentage of neighbors in the given neighborhood with size d. 
 | 
| HilOut
 Fast Outlier Detection in High Dimensional Spaces
 
 Outlier Detection using Hilbert space filling curves
 
 Reference:
 
 F. 
 | 
| HilOut.HilbertFeatures
 Class organizing the data points along a hilbert curve. 
 | 
| HilOut.HilFeature
 Hilbert representation of a single object. 
 | 
| HilOut.ScoreType
 Type of output: all scores (upper bounds) or top n only 
 | 
| KNNDD
 Nearest Neighbor Data Description. 
 | 
| KNNOutlier
 Outlier Detection based on the distance of an object to its k nearest
 neighbor. 
 | 
| KNNSOS
 kNN-based adaption of Stochastic Outlier Selection. 
 | 
| KNNWeightOutlier
 Outlier Detection based on the accumulated distances of a point to its k
 nearest neighbors. 
 | 
| LocalIsolationCoefficient
 The Local Isolation Coefficient is the sum of the kNN distance and the
 average distance to its k nearest neighbors. 
 | 
| ODIN
 Outlier detection based on the in-degree of the kNN graph. 
 | 
| ReferenceBasedOutlierDetection
 Reference-Based Outlier Detection algorithm, an algorithm that computes kNN
 distances approximately, using reference points. 
 | 
| SOS
 Stochastic Outlier Selection. 
 | 
Copyright © 2019 ELKI Development Team. License information.