Abstract
Graphs with labeled vertices and edges play an important role in various applications, including chemistry. A model of learning from positive and negative examples, naturally described in terms of Formal Concept Analysis (FCA), is used here to generate hypotheses about biological activity of chemical compounds. A standard FCA technique is used to reduce labeled graphs to object-attribute representation. The major challenge is the construction of the context, which can involve ten thousands attributes. The method is tested against a standard dataset from an ongoing international competition called Predictive Toxicology Challenge (PTC).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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., Wille, R.: Formal Concept Analysis: Mathematical foundations. Springer-Verlag, Heidelberg (1999)
Ganter, B., Kuznetsov, S.O.: Hypotheses and version spaces. In: Lex, W., de Moor, A., Ganter, B. (eds.) ICCS 2003. LNCS (LNAI), vol. 2746, pp. 83–95. Springer, Heidelberg (2003)
Kuznetsov, S.O.: Machine Learning and Formal Concept Analysis. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 287–312. Springer, Heidelberg (2004)
Kuznetsov, S.O., Finn, V.K.: On a model of learning and classification based on similarity operation. Obozrenie Prikladnoii Promyshlennoi Matematiki 3(1), 66–90 (1996) (in Russian)
Kuznetsov, S.O.: Learning of Simple Conceptual Graphs from Positive and Negative Examples. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 384–391. Springer, Heidelberg (1999)
Ganter, B., Kuznetsov, S.: Formalizing Hypotheses with Concepts. In: Ganter, B., Mineau, G. (eds.) ICCS 2000. LNCS (LNAI), vol. 1867, pp. 342–356. Springer, Heidelberg (2000)
Finn, V.K.: Plausible Reasoning in Systems of JSM Type. Itogi Nauki i Tekhniki, Seriya Informatika 15, 54–101 (1991) (in Russian)
Finn, V.K.: On Machine-Oriented Formalization of Plausible Reasoning in the Style of F. Backon–J. S. Mill. Semiotika i Informatika 20, 35–101 (1983) (in Russian)
Kuznetsov, S.O.: JSM-method as a machine learning method. Itogi Nauki i Tekhniki, ser. Informatika 15, 17–50 (1991) (in Russian)
Helma, C., King, R.D., Kramer, S., Srinvasan, A. (eds.): Proc. of the Workshop on Predictive Toxicology Challegnge at the 5th Conference on Data Mining and Knowledge Discovery (PKDD 2001) Freiburg (Germany) (September 7, 2001), http://www.predictivetoxicology.org/ptc/
McKay, B.D.: Practical graph isomorphism. Congressus Numerantium 30, 45–87 (1981)
Ullmann, J.R.: An algorithm for subgraph isomorphism. J. Assoc. Comput. Mach. 23, 31–42 (1976)
Grigoriev, P.A., Yevtushenko, S.A., Grieser, G.: QuDA, a data miner’s discovery enviornment. Technical Report AIDA 03 06, FG Intellektik, FB Informatik, Technische Universität Darmstadt (September 2003), http://www.intellektik.informatik.tudarmstadt.de/~peter/QuDA.pdf
Grigoriev, P., Yevtushenko, S.: JSM-Reasoning as a data mining tool. In: Proceedings of the 8th Russian National Conference on Artificial Intelligence, CAI-2002, pp. 112–122, Moscow (2002). PhysMathLit (in Russian)
DaMiT, the Data Mining online Tutorial, http://damit.dfki.de
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42, 203–231 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ganter, B., Grigoriev, P.A., Kuznetsov, S.O., Samokhin, M.V. (2004). Concept-Based Data Mining with Scaled Labeled Graphs. In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds) Conceptual Structures at Work. ICCS 2004. Lecture Notes in Computer Science(), vol 3127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27769-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-27769-9_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22392-4
Online ISBN: 978-3-540-27769-9
eBook Packages: Springer Book Archive