Abstract

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 oC soybe3n disc3Se when given a collection oC 4i descriptions oC dise3Sed soybeans h3ving one of Cour diseases. In a second problem. the method is used to find c3tegories underlying a collection of blocks-world structures. In 3 third problem. c3tegories of objects h3ving a more complex structure 3re determined 3nd contr3Stcd with categories gener3ted by people. The described method of conjunctive conceptual clustering Corms clusters oC objects (or situations) not on the b3Sis oC 3 numeric31 similarity me3Sure but on the b3Sis of the "conceptu31 cohesiveness " of one object to 3nother. The conceptu3l cohesiveness between two objects depends on the descriptions oC the two objects 3S well 3S the descriptions oC other ne3rby objects in the given collection 3nd concepts which are 3v3i13ble to describe object groups or object configurations 3S a whole. From a collection oC objects, some b3ckground domain knowledge, and a goal or purpose Cor clustering, conceptual clustering generates a hierarchic3l cl3Ssific3tion

Citations