A decision-directed clustering algorithm for discrete data

Authors:
Andrew K. C. Wong;T. S. Liu
Affiliations:
Carnegie-Mellon University, Pittsburgh, PA;School of Medicine, University of Pittsburgh, Pittsburgh, PA, and Montefiore Hospital, Pittsburgh, PA and Carnegie-Mellon University, Pittsburgh, PA
Venue:
IEEE Transactions on Computers
Year:
1977

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Abstract

This article presents a decision-directed approach for classifying discrete data. In the clustering algorithm, probable clusters are initiated through the use of a sorting scheme based on the estimated probability distribution of the data and an arbitrary distance measure. The subsequent iterative reclassification procedures are directed by the estimated distribution of each class. The distribution estimation adopted is modified from the dependence tree procedure. The algorithm performance is then evaluated through the use of simulated and clinical data. Finally, the algorithm is applied to disease categorization and to signs and symptoms ext.action for each disease class.