Generalized clustering, supervised learning, and data assignment

Authors:
Annaka Kalton;Pat Langley;Kiri Wagstaff;Jungsoon Yoo
Affiliations:
ISLE, Palo Alto, CA;ISLE, Palo Alto, CA;Cornell University, Ithaca, NY;Middle Tenn. State University, Murfreesboro, TN
Venue:
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2001

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Abstract

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.