Conjunctive Conceptual Clustering: A Methodology and Experimentation

摘要

This thesis describes a machine learning methodology called conjunctive conceptual clustering. The methodology can find conceptual patterns in data as illustrated by three sample problems. In one problem, the method is used to rediscover categories of soybean disease when given a collection of 47 descriptions of diseased soybeans having one of four diseases. In a second problem, the method is used to find categories underlying a collection of blocks-world structures. In a third problem, categories of objects having a more complex structure are determined and contrasted with categories generated by people. The described method of conjunctive conceptual clustering forms clusters of objects (or situations) not on the basis of a numerical similarity measure but on the basis of the conceptual cohesiveness of one object to another. The conceptual cohesiveness between two objects depends on the descriptions of the two objects as well as the descriptions of other nearby objects in the given collection and concepts which are available to describe object groups or object configurations as a whole.