A COMPARISON OF TWO NEW TECHNIQUES FOR CONCEPTUAL CLUSTERING

Abstract

Clustering, pattern recognition, and classification are important components of many artificial intelligence systems, especially those designed to classify new observations. Often, one has access to a history of old observations that have been previously classified and, under these circumstances, the old data may be used as a training set from which one may obtain rules for the subsequent classification of new data. The techniques used to obtain these rules may be traditional statistical methods or modern computer-intensive techniques. Sometimes, however, the history of old observations has not been previously classified. Under these circumstances, the analyst simply wishes to uncover structure in the data and ascertain whether the structure is apparent or real. When the analyst is searching for clusters, statistical clustering methodologies are often used. Although effective at locating clusters, such approaches leave the interpretation of the clusters as a task for the human analyst. A relatively new class of “conceptual” clustering techniques have emerged from the discipline of machine learning. These techniques attempt to both locate and explain clusters among the data. In this way, the explanations of cluster membership may be used to construct rules for the subsequent classification of new data. The generation of interpretable rules, whether by the use of classification algorithms or conceptual clustering algorithms is of considerable importance in reducing the knowledge acquisition “bottleneck” that often impedes progress towards the building of rule-based systems. In this paper, two new techniques for conceptual clustering are introduced and compared.