Fast Redescription Mining Using Locality-Sensitive Hashing
This work addresses a scalability bottleneck for researchers and practitioners in data analysis fields where redescription mining is applied, representing an incremental improvement over existing methods.
The paper tackles the computational intractability of redescription mining with many numerical attributes by introducing new algorithms based on locality-sensitive hashing, achieving orders of magnitude faster performance in matching and extension phases.
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.