  Redescription mining is a data analysis technique that has found applications
in diverse fields. The most used redescription mining approaches involve two
phases: finding matching pairs among data attributes and extending the pairs.
This process is relatively efficient when the number of attributes remains
limited and when the attributes are Boolean, but becomes almost intractable
when the data consist of many numerical attributes. In this paper, we present
new algorithms that perform the matching and extension orders of magnitude
faster than the existing approaches. Our algorithms are based on
locality-sensitive hashing with a tailored approach to handle the
discretisation of numerical attributes as used in redescription mining.
