  As data volumes continue to grow, clustering and outlier detection algorithms
are becoming increasingly time-consuming. Classical index structures for
neighbor search are no longer sustainable due to the "curse of dimensionality".
Instead, approximated index structures offer a good opportunity to
significantly accelerate the neighbor search for clustering and outlier
detection and to have the lowest possible error rate in the results of the
algorithms. Locality-sensitive hashing is one of those. We indicate directions
to model the properties of LSH.
