Pattern Classification for Mixed Feature-Type Symbolic Data Using Supervised Hierarchical Conceptual Clustering

This paper describes a region-oriented method of pattern classification based on the Cartesian system model (CSM), a mathematical model that allows manipulating mixed feature-type symbolic data. We use the supervised hierarchical conceptual clustering to generate class regions for respective pattern class based on the evaluation of the generality of the regions and the separability of the regions against other classes in each clustering step. We can easily find the robustly informative features to describe each pattern class against other pattern classes. Some examples show the effectiveness of the proposed method.