Clustering and Supervised Learning 3 3 . 3 Perceptron List Modeler

ABSTRACT Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising e ective but increasingly speci c clustering algorithms. In contrast, we have developed a generalized framework that accommodates diverse clustering algorithms in a systematic way. This framework views clustering as a general process of iterative optimization that includes modules for supervised learning and instance assignment. The framework has also suggested several novel clustering methods. In this paper, we investigate experimentally the e cacy of these algorithms and test some hypotheses about the relation between such unsupervised techniques and the supervised methods embedded in them.