Concept-based data mining with scaled labeled graphs

Bernhard Ganter

Sergei O. Kuznetsov

pp. 94-108

in: Karl E. Wolff, Heather D. Pfeiffer, Harry Delugach (eds), Conceptual structures at work, Berlin, Springer, 2004

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).