| 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.