
Publication details
Publisher: Springer
Place: Berlin
Year: 2004
Pages: 94-108
Series: Lecture Notes in Computer Science
ISBN (Hardback): 9783540223924
Full citation:
, "Concept-based data mining with scaled labeled graphs", in: Conceptual structures at work, Berlin, Springer, 2004


Concept-based data mining with scaled labeled graphs
pp. 94-108
in: Karl E. Wolff, Heather D. Pfeiffer, Harry Delugach (eds), Conceptual structures at work, Berlin, Springer, 2004Abstract
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
Publisher: Springer
Place: Berlin
Year: 2004
Pages: 94-108
Series: Lecture Notes in Computer Science
ISBN (Hardback): 9783540223924
Full citation:
, "Concept-based data mining with scaled labeled graphs", in: Conceptual structures at work, Berlin, Springer, 2004