期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2020
卷号：30
期号：1
页码：161-171
DOI：10.34768/amcs-2020-0013
出版社：De Gruyter Open
摘要：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.
关键词：biclustering; Boolean reasoning; prime implicant approximation; biomedical data analysis; Johnson heuristic
