A single-pass triclustering algorithm
Dmitry Gnatyshak · Automatic Documentation and Mathematical Linguistics · 2015
As the popularity of the field of big data continues to rise, the problem of the development of efficient algorithms with low time complexity and that ability to be parallelized is more and more frequently posed. This work is aimed at the development of an efficient single-pass algorithm for the triclustering of binary data that is suitable for use in the field of big data. As a result, a single-pass serial online OAC triclustering algorithm (triclustering of object-attribute-condition) was obtained. This algorithm not only has a very low complexity in time and memory, but it also can be effectively parallelized.