Abstract
A learning model is considered in terms of formal concept analysis (FCA). This model is generalized for objects represented by sets of graphs with partially ordered labels of vertices and edges (these graphs can be considered as simple conceptual graphs). An algorithm that computes all concepts and the linear (Hasse) diagram of the concept lattice in time linear with respect to the number of concepts is presented. The linear diagram gives the structure of the set of all concepts with respect to the partial order on them and provides a useful tool for browsing or discovery of implications (associations) in data.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Carpineto, C., Romano, G.: A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval. Machine Learning 24, 95–122 (1996)
Finn, V.K.: On Machine-Oriented Formalization of Plausible Reasoning in the Style of F. Backon-J. S. Mill. Semiotika Informatika 20, 35–101 (1983) (in Russian)
Finn, V.K.: Plausible Reasoning in Systems of JSM Type. Itogi Nauki i Tekhniki, ser. Informatika 15, 54–101 (1991)
Ganter, B.: Algorithmen zur Formalen Begriffsanalyse. In: Ganter, B., Wille, R., and Wolff, K. E. (eds.): Beitráge zur Begriffsanalyse. B. I. Wissenschaftsverlag, Mannheim, 241-254 (1987)
Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, Heidelberg (1999)
Godin, R., Missaoui, R., Alaoui, H.: Incremental Concept Formation Algorithms Based on Galois (Concept) Lattices. Computational Intelligence 11(2), 246–267 (1995)
Guénoche, A.: Construction du treillis de Galois d’une relation binaire. Math. Sci. Hum. 95, 5–18 (1990)
Kuznetsov, S.O.: Interpretation on Graphs and Algorithmic Complexity Charac- teristics of a Search of Specific Patterns. Autom. Document. Math. Ling. 23(1), 37–45 (1989)
Kuznetsov, S.O.: JSM-method as a Machine Learning System. Itogi Nauki Tekhn., ser. Informat. 15, 17–54 (1991) (in Russian)
Mugnier, M.L.: On Generalization/Specialization for Conceptual Graphs. J. Exp. Theor. Artif. Intel. 7, 325–344 (1995)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Pruning Closed Itemset Lattices for Association Rules. In: Proc. 14th BDA Conference on Advanced Databases (BDA 1998), Hamammet, pp. 177–196 (1998)
Skorsky, M.: Endliche Verbände - Diagramme und Eigenschaften. Shaker, Aachen (1992)
Sowa, J.F.: Conceptual Structures - Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)
Wille, R.: Restructuring Lattice Theory: an Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Sets. Reidel, Dordrecht, pp. 445–470 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuznetsov, S.O. (1999). Learning of Simple Conceptual Graphs from Positive and Negative Examples. In: Żytkow, J.M., Rauch, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science(), vol 1704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48247-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-48247-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66490-1
Online ISBN: 978-3-540-48247-5
eBook Packages: Springer Book Archive