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Dynamic Boolean Matrix Factorizations


Miettinen,  Pauli
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Miettinen, P. (2012). Dynamic Boolean Matrix Factorizations. In M. J. Zaki, A. Siebes, J. X. Yu, B. Goethals, G. Webb, & X. Wu (Eds.), Proceedings of the 12th IEEE International Conference on Data Mining (pp. 519-528). Los Alamitos, CA: IEEE Computer Society.

Boolean matrix factorization is a method to de- compose a binary matrix into two binary factor matrices. Akin to other matrix factorizations, the factor matrices can be used for various data analysis tasks. Many (if not most) real-world data sets are dynamic, though, meaning that new information is recorded over time. Incorporating this new information into the factorization can require a re-computation of the factorization – something we cannot do if we want to keep our factorization up-to-date after each update. This paper proposes a method to dynamically update the Boolean matrix factorization when new data is added to the data base. This method is extended with a mechanism to improve the factorization with a trade-off in speed of computation. The method is tested with a number of real-world and synthetic data sets including studying its efficiency against off-line methods. The results show that with good initialization the proposed online and dynamic methods can beat the state- of-the-art offline Boolean matrix factorization algorithms.