An extended system for conceptual clustering

Abstract

CLUSTER/2 (Michalski, 1980a, Stepp&Michalski, 1986) in a conceptual clustering system, having the great advantage that obtained clusters are represented in the formof symbolic expressions. However, it has some disadvantages. In this article, a modified version of CLUSTER/2 is proposed. Background knowledge can be conveyed to the system through semantic networks; differentiation among objects is calculating using semantic distance. A different quality evaluation is used to measure the quality of clustering in a more sensible way. The order dependence problem of overlap resolution is eliminated with a fuzzy k-nearest neighborhood technique. Finally, a hill-climbing algorithm is applied to determine the number of clusters automatically. These improvements provide a more stable and user-friendly clustering environment for the user, without changing the system architecture of CLUSTER/2.