Abstract
Boolean matrix factorization (BMF) is a well-established data analytical method whose goal is to decompose a single large matrix into two, preferably smaller, matrices, carrying the same or similar information as the original matrix. In essence, it can be used to reduce data dimensionality and to provide fundamental insight into data. Existing algorithms are often negatively affected by the presence of noise in the data, which is a common case for real-world datasets. We present an initial study on an algorithm for approximate BMF that uses association rules in a novel way to identify possible noise. This allows us to suppress the impact of noise and improve the quality of results. Moreover, we show that association rules provide a suitable framework allowing the handling of noise in BMF in a justified way.
P. Krajča—was supported by the grant JG 2019 of Palacký University Olomouc, No. JG_2019_008. Martin Trnecka was supported by the grant JG 2020 of Palacký University Olomouc, No. JG_2020_003. Support by Grant No. IGA_PrF_2020_019 of IGA of Palacký University is also acknowledged.
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Krajča, P., Trnecka, M. (2021). Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules. In: Abreu, P.H., Rodrigues, P.P., Fernández, A., Gama, J. (eds) Advances in Intelligent Data Analysis XIX. IDA 2021. Lecture Notes in Computer Science(), vol 12695. Springer, Cham. https://doi.org/10.1007/978-3-030-74251-5_29
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