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).
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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
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DOI: https://doi.org/10.1007/978-3-540-27769-9_6
Publisher Name: Springer, Berlin, Heidelberg
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