Abstract
Boolean matrix factorization (BMF) is a popular data analysis method summarizing the input data by Boolean factors. The Boolean nature ensures an easy interpretation of a particular factor, however, the interpretation of all discovered factors (as a whole) by domain experts may be difficult as the BMF methods seek only information in the data and do not reflect the experts understanding of data. In the paper, we propose a formalization of a novel variant of BMF reflecting expert’s background knowledge—additional knowledge about the data—that is not part of the data, in the form of attribute weights, as well as an algorithm for it. Moreover, we show that the proposed algorithm, which significantly outperforms the state-of-the-art algorithm, provides encouraging results that are worth further investigation.
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