A Concept Lattice Method for Eliminating Redundant Features
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Publisher
Springer International Publishing
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https://link.springer.com/content/pdf/10.1007/978-3-030-69992-5_4
Reference19 articles.
1. Kaytoue-Uberall, M., Duplessis, S., Napoli, A.: Using formal concept analysis for the extraction of groups of co-expressed genes. In: Le Thi, H.A., Bouvry, P., Pham Dinh, T. (eds.) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2008. Communications in Computer and Information Science, vol. 14, pp. 439–449. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87477-5_47
2. Kaytoue M., Duplessis S., Kuznetsov S.O., Napoli A. (2009) Two FCA-based methods for mining gene expression data. In: Ferré, S., Rudolph, S. (eds.) Formal Concept Analysis. ICFCA 2009. Lecture Notes in Computer Science, vol. 5548, pp. 251–266. Springer, Heidelberg. https://doi.org/10.1007/978-3-642-01815-2_19
3. Wang, D., Cui, L., Wang, Y., Yuan, H., Zhang, J.: Association Rule mining based on concept lattice in bioinformatics research. In: 2010 International Conference on Biomedical Engineering and Computer Science, pp. 1–4. IEEE, April 2010
4. Keller, B.J., Eichinger, F., Kretzler, M.: Formal concept analysis of disease similarity. AMIA Summits Transl. Sci. Proc. 2012, 42 (2012)
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