Donald Bren School of Information and Computer Sciences

This thesis presents a careful experimental examination of the task of concept formation. This learning task involves acquiring concepts or classes that group observed instances. These classes can then be used for the recognition or classification of unseen instances and for predicting values of unknown attributes. To carry out this task, I present CLASSIT, a robust, general-purpose system for incremental, unsupervised concept learning. In order to test the system, the thesis reports on a variety of experimental results. There are two broad categories for the experiments in this thesis: experiments that test the ability of the system with different input data, and experiments that hold the input data constant and test various components of the system by changing the system itself. The latter include parametric studies and comparative studies with cluster analysis methods. The largest modification I suggest for the basic concept formation algorithm is the addition of an attention mechanism. This lets the system focus attention on only the most important features (or attributes) of an instance. With attention, instances can be more quickly recognized, since the less important attributes are simply ignored and treated as missing. Finally, although the accomplishments of the CLASSIT system are modest, I conclude by discussing the potential for using the system as part of a larger cognitive architecture.