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). © Springer-Verlag 2004 References.
CITATION STYLE
Ganter, B., Grigoriev, P. A., Kuznetsov, S. O., & Samokhin, M. V. (2004). Concept-based data mining with scaled labeled graphs. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3127, 94–108. https://doi.org/10.1007/978-3-540-27769-9_6
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