During the past few years Boolean matrix factorization (BMF) has become an
important direction in data analysis. The minimum description length principle
(MDL) was successfully adapted in BMF for the model order selection.
Nevertheless, a BMF algorithm performing good results from the standpoint of
standard measures in BMF is missing. In this paper, we propose a novel
from-below Boolean matrix factorization algorithm based on formal concept
analysis. The algorithm utilizes the MDL principle as a criterion for the
factor selection. On various experiments we show that the proposed algorithm
outperforms---from different standpoints---existing state-of-the-art BMF
algorithms
