document: Generalized clustering , supervised learning , and data assignment Clustering algorithms have become increasingly important in handling and analyzing data . Considerable work has been done in devising effective but increasingly specific 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 efficacy of these algorithms and test some hypotheses about the relation between such unsupervised techniques and the supervised methods embedded in them .
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