How can I cluster data using a distance matrix with the ELKI library? about elki HOT 4 CLOSED

AbstractNumberVectorDistanceFunction is the approriate parent class only if your input data are number vectors. If your data type is abstract object identifiers, subclass AbstractDBIDRangeDistanceFunction. You then have to implement double distance(int i1, int i2);

There are already different implementations of a distance function for precomputed distances, for example DiskCacheBasedDoubleDistanceFunction that memory-maps a distance matrix stored on disk. We should add a DoubleMatrixDistanceFunction though, for direct use from Java.

See also: https://elki-project.github.io/howto/precomputed_distances

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Does this support asymmetric matrices?

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A distance is supposed to be symmetric; it may or may not work with asymmetric distances.
The current implementations will likely assume symmetry to reduce memory usage by 50%.

But it even more depends on the algorithm. Many will assume distances are symmetric, and asymmetric distances can likely cause infinite loops etc. Some parts of the code may be switching x and y if they have reason to assume that one is faster than the other because of caches etc.

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Alright. Thanks a lot!

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