Conceptual Clustering: Inventing Goal-Oriented Classifications of Structured Objects

Abstract

INTRODUCTION Creating a classification is typically the first step in developing a theory about a collection of observations or phenomena. This process is a form of learning from observation (learning without a teacher), and its goal is to structure given observa- tions i.nto a hierarchy of meaningful categories. The problem of automatically ereat ing such a hierarchy has so far received little attention in AI. Yet creating classifications is a very basic and widely practiced intellectual process. Past work on this problem was done mostly outside AI under the headings of numerical taxonomy and cluster analysis (Anderberg, 1973). Those methods are based on the application of a mathematical measure of similarity between objects, defined over a finite, a priori given set of object attributes. Classes of objects are taken as collections of objects with high intraclass and low interclass similarity. The methods assume that objects .are characterized by sequences of attribute/value pairs an