How to do … in ELKI
 Invoke ELKI from within Java
 Use index structures for accelerating data mining algorithms
 Process geo data in ELKI
 Perform geospatial outlier detection with an external neighborhood specification
 Precompute distance matrixes and use external distances
 Using sparse vector data
 more to come
See also the Tutorial section:
 Implementing a custom distance function, a variable exponent Minkowskinorm
 Implementing a new outlier detection algorithm, using the distances standard deviation
 Implementing a kmeans clustering variant, producing clusters of the same size
and Examples:

Greedy outlier ensemble computes a large set of outlier detection methods, then constructs and evaluates a greedy ensemble based on these methods.

Cluster evaluation with segmented view detailes the configurations used for the ICDE 2012 demonstration on cluster evaluation.