Efficient Group of Permutants for Proximity Searching
Karina Figueroa Mora, Rodrigo Paredes, and Roberto Rangel
Modeling proximity searching problems in a metric space allows one to
approach many problems in different areas, e.g. pattern recognition,
multimedia search, or clustering. Recently there was proposed the
permutation based approach, a novel technique
that is unbeatable in practice but difficult to compress. In this
article we introduce an improvement on that metric space search data
structure.
Our technique shows that we can compress the permutation based algorithm
without loosing precision. We show experimentally that our technique is
competitive with the original idea and improves it up to 46% in real
databases.