Similarity join is a key operation in metric databases. It retrieves all pairs
of elements that are similar. Solving such a problem usually requires comparing
every pair of objects of the datasets, even when indexing and ad hoc algorithms
are used. We propose a simple and efficient algorithm for the computation of
the approximated nearest neighbor self-similarity join. This algorithm computes
O(n^(3/2)) distances and it is empirically shown that it reaches an empirical
precision of 46% in real-world datasets. We provide a comparison to other
common techniques such as Quickjoin and Locality-Sensitive Hashing and argue
that our proposal has a better execution time and average precision.