Conceptual Clustering: Discovering Patterns from Chaotic Reality and Pattern-Directed Decisionmaking

Abstract

Human expert decision makers can be characterized by their ability to perceive a hypothetical conceptual pattern underlying a given collection of objects. The conventional cluster analysis is insufficient to generate such patterns since its clustering process is far from what the human decision makers actually do in inductively forming some concepts from individual observations based on the “meaning” of the objects and the clusters. In this paper, by introducing an idea of prototype theory from the psychological domain with respect to human category formation, an alternative methodology of conceptual clustering is presented. The algorithm can be roughly divided into two phases; an inductive prototype formation from training samples in a bottom-up way and a pattern-directed clustering of the instances being affected by the acquired concepts in a top-down fashion. Using the schematically-modeled example, the algorithm is illustrated as well as the clustering results.