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(2004) Conceptual structures at work, Dordrecht, Springer.
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).
Publication details
DOI: 10.1007/978-3-540-27769-9_6
Full citation:
Ganter, B. , Kuznetsov, S. O. (2004)., Concept-based data mining with scaled labeled graphs, in K. E. Wolff, H. D. Pfeiffer & H. Delugach (eds.), Conceptual structures at work, Dordrecht, Springer, pp. 94-108.
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