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Description

| Class Summary | |
|---|---|
| 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 2005 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
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