See: Description
| Class | Description |
|---|---|
| AbstractDistanceBasedSpatialOutlier<N,O,D extends NumberDistance<D,?>> |
Abstract base class for distance-based spatial outlier detection methods.
|
| AbstractDistanceBasedSpatialOutlier.Parameterizer<N,O,D extends NumberDistance<D,?>> |
Parameterization class.
|
| AbstractNeighborhoodOutlier<O> |
Abstract base class for spatial outlier detection methods using a spatial
neighborhood.
|
| AbstractNeighborhoodOutlier.Parameterizer<O> |
Parameterization class.
|
| CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?>,D extends NumberDistance<D,?>> |
GLS-Backward Search is a statistical approach to detecting spatial outliers.
|
| CTLuGLSBackwardSearchAlgorithm.Parameterizer<V extends NumberVector<?>,D extends NumberDistance<D,?>> |
Parameterization class
|
| CTLuMeanMultipleAttributes<N,O extends NumberVector<?>> |
Mean Approach is used to discover spatial outliers with multiple attributes.
|
| CTLuMeanMultipleAttributes.Parameterizer<N,O extends NumberVector<?>> |
Parameterization class.
|
| CTLuMedianAlgorithm<N> |
Median Algorithm of C.
|
| CTLuMedianAlgorithm.Parameterizer<N> |
Parameterization class.
|
| CTLuMedianMultipleAttributes<N,O extends NumberVector<?>> |
Median Approach is used to discover spatial outliers with multiple
attributes.
|
| CTLuMedianMultipleAttributes.Parameterizer<N,O extends NumberVector<?>> |
Parameterization class.
|
| CTLuMoranScatterplotOutlier<N> |
Moran scatterplot outliers, based on the standardized deviation from the
local and global means.
|
| CTLuMoranScatterplotOutlier.Parameterizer<N> |
Parameterization class.
|
| CTLuRandomWalkEC<N,D extends NumberDistance<D,?>> |
Spatial outlier detection based on random walks.
|
| CTLuRandomWalkEC.Parameterizer<N,D extends NumberDistance<D,?>> |
Parameterization class.
|
| CTLuScatterplotOutlier<N> |
Scatterplot-outlier is a spatial outlier detection method that performs a
linear regression of object attributes and their neighbors average value.
|
| CTLuScatterplotOutlier.Parameterizer<N> |
Parameterization class.
|
| CTLuZTestOutlier<N> |
Detect outliers by comparing their attribute value to the mean and standard
deviation of their neighborhood.
|
| CTLuZTestOutlier.Parameterizer<N> |
Parameterization class.
|
| SLOM<N,O,D extends NumberDistance<D,?>> |
SLOM: a new measure for local spatial outliers
Reference:
Sanjay Chawla and Pei Sun SLOM: a new measure for local spatial outliers in Knowledge and Information Systems 9(4), 412-429, 2006 This implementation works around some corner cases in SLOM, in particular when an object has none or a single neighbor only (albeit the results will still not be too useful then), which will result in divisions by zero. |
| SLOM.Parameterizer<N,O,D extends NumberDistance<D,?>> |
Parameterization class.
|
| SOF<N,O,D extends NumberDistance<D,?>> |
The Spatial Outlier Factor (SOF) is a spatial
LOF variation. |
| SOF.Parameterizer<N,O,D extends NumberDistance<D,?>> |
Parameterization class
|
| TrimmedMeanApproach<N> |
A Trimmed Mean Approach to Finding Spatial Outliers.
|
| TrimmedMeanApproach.Parameterizer<N> |
Parameterizer.
|
Spatial outlier detection algorithms