1. Asses, Y., Buzmakov, A., Bourquard, T., Kuznetsov, S. O., & Napoli, A. (2012). A hybrid classification approach based on FCA and emerging patterns—an application for the classification of biological inhibitors. In Proceedings of the 9th international conference on concept lattices and their applications, pp. 211–222.
2. Banerjee, A., Dhillon, I. S., Ghosh, J., Merugu, S., & Modha, D. S. (2007). A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. Journal of Machine Learning Research, 8, 1919–1986.
3. Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., & Zitzler, E. (2006). BicAT: a biclustering analysis toolbox. Bioinformatics, 22(10), 1282–1283.
4. Belohlávek, R., & Vychodil, V. (2010). Discovery of optimal factors in binary data via a novel method of matrix decomposition. Journal of Computer and System Sciences, 76(1), 3–20.
5. Belohlávek, R., Baets, B. D., Outrata, J., & Vychodil, V. (2009). Inducing decision trees via concept lattices. International Journal of General Systems, 38(4), 455–467.