A Hybrid Conceptual Clustering System

Conceptual clustering is used to organize observations into an abstract hierarchy which can be used to predict classes and/or attribute values. Typically this clustering has been done using either incremental or nonincrementallearning. Incremental learning suffers from the ordering problem, while nonincremental learning cannot handle a dynamic environment efficiently. This paper proposes a hybrid conceptual clustering system which uses two learning algorithms. The first stage is based on Genetic Algorithms and is used as a preprocessor for the second stage. The second stage is an incremental learning system. This hybrid conceptual clustering system overcomes the difficulties encountered by using either a nonincremental or an incremental learning system alone.