| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski |
Minkowski space L_p norms such as the popular Euclidean and Manhattan distances.
|
| Class and Description |
|---|
| LPNormDistanceFunction
Provides a LP-Norm for FeatureVectors.
|
| Class and Description |
|---|
| EuclideanDistanceFunction
Provides the Euclidean distance for FeatureVectors.
|
| LPNormDistanceFunction
Provides a LP-Norm for FeatureVectors.
|
| ManhattanDistanceFunction
Manhattan distance function to compute the Manhattan distance for a pair of
FeatureVectors.
|
| MaximumDistanceFunction
Maximum distance function to compute the Maximum distance for a pair of
FeatureVectors.
|
| MinimumDistanceFunction
Maximum distance function to compute the Minimum distance for a pair of
FeatureVectors.
|
| SparseEuclideanDistanceFunction
Euclidean distance function.
|
| SparseLPNormDistanceFunction
Provides a LP-Norm for FeatureVectors.
|
| SparseManhattanDistanceFunction
Manhattan distance function.
|
| SparseMaximumDistanceFunction
Maximum distance function.
|
| SquaredEuclideanDistanceFunction
Provides the squared Euclidean distance for FeatureVectors.
|