Abstract
We propose a unifying FCA-based framework for some questions in data analysis and data mining, combining ideas from Rough Set Theory, JSM-reasoning, and feature selection in machine learning. Unlike the standard rough set model the indiscernibility relation in our paper is based on a quasi-order, not necessarily an equivalence relation. Feature selection, though algorithmically difficult in general, appears to be easier in many cases of scaled many-valued contexts, because the difficulties can at least partially be projected to the scale contexts. We propose a heuristic algorithm for this.
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Ferré, S., Ridoux, O.: The Use of Associative Concepts in the Incremental Building of a Logical Context. In: Priss, U., Corbett, D.R., Angelova, G. (eds.) ICCS 2002. LNCS (LNAI), vol. 2393, pp. 299–313. Springer, Heidelberg (2002)
Finn, V.K.: On Machine-Oriented Formalization of Plausible Reasoning in the Style of F. Backon–J. S. Mill (in Russian). Semiotika Informatika 20, 35–101 (1983)
Finn, V.K.: Plausible Reasoning in Systems of JSM Type (in Russian). Itogi Nauki i Tekhniki, Seriya Informatika 15, 54–101 (1991)
Finn, V.K.: Synthesis of cognitive procedures and the problem of induction (in Russian). Nauchno-Tekhnicheskaya Informatsiya 2(1-2), 8–44 (1999)
Ganter, B., Kuznetsov, S.O.: Formalizing Hypotheses with Concepts. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 342–356. Springer, Heidelberg (2000)
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)
Ganter, B., Kuznetsov, S.O.: Hypotheses and Version Spaces. In: Ganter, B., de Moor, A., Lex, W. (eds.) ICCS 2003. LNCS, vol. 2746, pp. 83–95. Springer, Heidelberg (2003)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
Garey, M.R., Johnson, D.S.: Computers and Intractability. A Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)
Grigoriev, P.A., Kuznetsov, S.O., Obiedkov, S.A., Yevtushenko, S.A.: On a Version of Mill’s Method of Difference. In: Proc. ECAI 2002 Workshop on Concept Lattices in Data Mining, Lyon, pp. 26–31 (2002)
Kuznetsov, S.O.: JSM-method as a Machine Learning System. Itogi Nauki i Tekhniki, ser. Informatika 15, 17–54 (1991)
Kuznetsov, S.O., Samokhin, M.V.: Learning Closed Sets of Labeled Graphs for Chemical Applications. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 190–208. Springer, Heidelberg (2005)
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. In: The Springer International Series in Engineering and Computer Science, vol. 454, Springer, New York (1998)
Mitchell, T.: Machine Learning, The McGraw-Hill Companies (1997)
Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: Probabilistic versus deterministic approach. International Journal of Man-Machine Studies 29, 81–95 (1988)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing, Dordrecht (1991)
Wolski, M.: Galois connections and data analysis. Fundamenta Informatica 60, 401–415 (2004)
Ziarko, W.: Rough sets as a methodology for data mining. In: Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 554–576. Physica-Verlag, Heidelberg (1998)
Ziarko, W., Shan, N.: Discovering attribute relationships, dependencies and rules by using rough sets. In: Proc. 28th Annual Hawaii International Conference on System Sciences (HICSS 1995), pp. 293–299 (1995)
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Ganter, B., Kuznetsov, S.O. (2008). Scale Coarsening as Feature Selection. In: Medina, R., Obiedkov, S. (eds) Formal Concept Analysis. ICFCA 2008. Lecture Notes in Computer Science(), vol 4933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78137-0_16
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DOI: https://doi.org/10.1007/978-3-540-78137-0_16
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