Abstract
Boolean matrix decomposition is a method to obtain a compressed representation of a matrix with Boolean entries. We present a modular framework that unifies several Boolean matrix decomposition algorithms, and provide methods to evaluate their performance. The main advantages of the framework are its modular approach and hence the flexible combination of the steps of a Boolean matrix decomposition and the capability of handling missing values. The framework is licensed under the GPLv3 and can be downloaded freely at http://projects.informatik.uni-mainz.de/bmad .
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Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Lu, H.: Boolean matrix decomposition and extension with applications. PhD thesis, Rutgers University (2011)
Miettinen, P.: The Boolean column and column-row matrix decompositions. Data Mining and Knowledge Discovery 17(1), 39–56 (2008)
Miettinen, P., et al.: Matrix decomposition methods for data mining: Computational complexity and algorithms. PhD thesis, University of Helsinki (2009)
Miettinen, P., Mielikainen, T., Gionis, A., Das, G., Mannila, H.: The discrete basis problem. IEEE Transactions on Knowledge and Data Engineering 20(10), 1348–1362 (2008)
Shen, B.-H., Ji, S., Ye, J.: Mining discrete patterns via binary matrix factorization. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 757–766. ACM (2009)
Streich, A.P., Frank, M., Basin, D., Buhmann, J.M.: Multi-assignment clustering for Boolean data. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 969–976. ACM (2009)
Vaidya, J.: Boolean matrix decomposition problem: Theory, variations and applications to data engineering. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 1222–1224. IEEE (2012)
Wicker, J., Pfahringer, B., Kramer, S.: Multi-label classification using Boolean matrix decomposition. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186. ACM (2012)
Zhang, Z.-Y., Li, T., Ding, C., Ren, X.-W., Zhang, X.-S.: Binary matrix factorization for analyzing gene expression data. Data Mining and Knowledge Discovery 20(1), 28–52 (2010)
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Tyukin, A., Kramer, S., Wicker, J. (2014). BMaD – A Boolean Matrix Decomposition Framework. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_40
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DOI: https://doi.org/10.1007/978-3-662-44845-8_40
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