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Paper details
Number 1 - March 2020
Volume 30 - 2020
Heuristic search of exact biclusters in binary data
Marcin Michalak, Roman Jaksik, Dominik Ślęzak
Abstract
The biclustering of two-dimensional homogeneous data consists in finding a subset of rows and a subset of columns whose
intersection provides a set of cells whose values fulfil a specified condition. Usually it is defined as equality or comparability.
One of the presented approaches is based on the model of Boolean reasoning, in which finding biclusters in binary or discrete
data comes down to the problem of finding prime implicants of some Boolean function. Due to the high computational
complexity of this task, the application of some heuristics should be considered. In the paper, a modification of the well-known Johnson strategy for prime implicant approximation induction is presented, which is necessary for the biclustering
problem. The new method is applied to artificial and biomedical datasets.
Keywords
biclustering, Boolean reasoning, prime implicant approximation, biomedical data analysis, Johnson heuristic