AN ALGEBRAIC APPROACH TO INDUCTIVE LEARNING

The paper presents a framework to induction of concept hierarchies based on consistent integration of metric and similarity-based approaches. The hierarchies used are subsumption lattices induced by the least general generalization operator (lgg) commonly used in inductive learning. Using some basic results from lattice theory the paper introduces a semantic distance measure between objects in concept hierarchies and discusses its applications for solving concept learning and conceptual clustering tasks. Experiments with well known ML datasets represented in three types of languages - propositional (attribute-value), atomic formulae and Horn clauses, are also presented.