Abstract
This paper holds on the application of two classification methods based on formal concept analysis (FCA) to interval data. The first method uses a similarity between objects while the second considers so-called pattern structures. We deeply detail these methods in order to show their close links. This parallel study helps understanding complex data with concept lattices. We explain how the second method obtains same results and how to handle missing values. Most importantly, this is achieved in full compliance with the FCA-framework, and thus benefits from existing and efficient tools such as algorithms. Finally, an experiment on real-world data in agronomy has been carried out for decision helping in agricultural practices.
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References
Barbut, M., Monjardet, B.: Ordre et Classification, Algèbre et Combinatoire. Hachette, Paris (1970)
Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical foundations edn. Springer, Heidelberg (1999)
Wille, R.: Why can concept lattices support knowledge discovery in databases? J. Exp. Theor. Artif. Intell. 14(2-3), 81–92 (2002)
Kuznetsov, S.O., Obiedkov, S.A.: Comparing Performance of Algorithms for Generating Concept Lattices. J. Exp. Theor. Artif. Intell. 14, 189–216 (2002)
Valtchev, P., Missaoui, R., Godin, R.: Formal concept analysis for knowledge discovery and data mining: The new challenges. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 352–371. Springer, Heidelberg (2004)
Pensa, R.G., Leschi, C., Besson, J., Boulicaut, J.F.: Assessment of discretization techniques for relevant pattern discovery from gene expression data. In: Zaki, M.J., Morishita, S., Rigoutsos, I. (eds.) BIOKDD, pp. 24–30 (2004)
Kaytoue, M., Duplessis, S., Napoli, A.: Using formal concept analysis for the extraction of groups of co-expressed genes. In: An, L.T.H., Bouvry, P., Tao, P.D. (eds.) MCO. CCIS, vol. 14, pp. 439–449. Springer, Heidelberg (2008)
Messai, N.: Formal Concept Analysis guided by Domain Knowledge: Application to genomic resources discovery on the Web (in French). PhD Thesis in Computer Science, University Henri Poincaré – Nancy 1, France (March 2009)
Messai, N., Devignes, M.D., Napoli, A., Smail-Tabbone, M.: Many-valued concept lattices for conceptual clustering and information retrieval. In: Ghallab, M., et al. (eds.) Proc. of 18th European Conference on Artificial Intelligence, pp. 127–131 (2008)
Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001)
Kuznetsov, S.O.: Galois connections in data analysis: Contributions from the soviet era and modern russian research. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 196–225. Springer, Heidelberg (2005)
Chaudron, L., Maille, N.: Generalized formal concept analysis. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 357–370. Springer, Heidelberg (2000)
Ferré, S., Ridoux, O.: A Logical Generalization of Formal Concept Analysis. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 357–370. Springer, Heidelberg (2000)
Kaytoue, M., Duplessis, S., Kuznetsov, S.O., Napoli, A.: Two FCA-Based Methods for Mining Gene Expression Data. In: Ferré, S., Rudolph, S. (eds.) ICFCA 2009. LNCS, vol. 5548, pp. 251–266. Springer, Heidelberg (2009)
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, Boston (1982)
Raedt, L.D.: 9: Kernels and Distances for Strucutred Data. In: Logical and Relational Learning, pp. 289–324 (2008)
Van der Werf, H., Zimmer, C.: An indicator of pesticide environmental impact based on a fuzzy expert system. Chemosphere 36(10), 2225–2249 (1998)
Bockstaller, C., Girardin, P., van der Werf, H.: Use of agro-ecological indicators for the evaluation of farming systems. European Journal of Agronomy 7(1-3), 261–270 (1997)
Assaghir, Z., Girardin, P., Napoli, A.: Fuzzy logic approach to represent and propagate imprecision in agri-environmental indicator assessment. In: Proc. of the European Society For Fuzzy Logic And Technology Conference (2009)
Dubois, D., de Saint-Cyr, F.D., Prade, H.: A possibility-theoretic view of formal concept analysis. Fundamenta Informaticae 75(1-4), 195–213 (2007)
Guillas, S., Bertet, K., Visani, M., Ogier, J.M., Girard, N.: Some links between decision tree and dichotomic lattice. In: CLA 2008, pp. 193–205 (2008)
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Kaytoue, M., Assaghir, Z., Messai, N., Napoli, A. (2010). Two Complementary Classification Methods for Designing a Concept Lattice from Interval Data. In: Link, S., Prade, H. (eds) Foundations of Information and Knowledge Systems. FoIKS 2010. Lecture Notes in Computer Science, vol 5956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11829-6_22
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DOI: https://doi.org/10.1007/978-3-642-11829-6_22
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