Distance Functions
ELKI release 0.6.0~20141030 includes the following distance functions
- Minkowski family:
- EuclideanDistanceFunction
- ManhattanDistanceFunction
- LPNormDistanceFunction
- MaximumDistanceFunction
- MinimumDistanceFunction
- SquaredEuclideanDistanceFunction
- WeightedLPNormDistanceFunction
- WeightedEuclideanDistanceFunction
- WeightedManhattanDistanceFunction
- WeightedMaximumDistanceFunction
- WeightedSquaredEuclideanDistanceFunction
- ArcCosineDistanceFunction
- CosineDistanceFunction
- RandomStableDistanceFunction
- BrayCurtisDistanceFunction
- CanberraDistanceFunction
- WeightedCanberraDistanceFunction
- ClarkDistanceFunction
- Kulczynski1DistanceFunction
- LorentzianDistanceFunction
- SharedNearestNeighborJaccardDistanceFunction
- LocallyWeightedDistanceFunction
- Optimized implementations for sparse vectors:
- Adapters for similarity functions:
- Distances for probability distributions:
- Distance functions for 1-dimensional histograms:
- Color histogram distance functions:
- Correlation distance functions:
- PearsonCorrelationDistanceFunction
- SquaredPearsonCorrelationDistanceFunction
- AbsolutePearsonCorrelationDistanceFunction
- UncenteredCorrelationDistanceFunction
- SquaredUncenteredCorrelationDistanceFunction
- AbsoluteUncenteredCorrelationDistanceFunction
- WeightedPearsonCorrelationDistanceFunction
- WeightedSquaredPearsonCorrelationDistanceFunction
- Set-based distance functions (for binary data):
- String distance functions:
- Spatial distance functions (for geo data mining):
- External distance adapters (to access precomputed and externally computed distances):
- Subspace distance functions:
- Time series distance functions:
- Distance functions for comparing clusters:
Similarity Functions
Similarity functions usable through the adapter classes above are:
Implementing custom distance funtions
When implementing custom distance functions, ask yourself the following questions first:
- Is it defined on the data itself (like euclidean distance) or on the instances (precomputed, external, second order distances)?
- What requirements does it have on the input data?
- What is the output data type?
Most likely, you will be implementing a PrimitiveDistanceFunction and can save yourself some work by deriving from AbstractPrimitiveDistanceFunction, for example for distances defined in coordinate vectors.
The Tutorial on writing a custom distance function takes you through all the steps needed for implementing a custom distance function.