EVALUATING CLUSTERING METHODS FOR PSYCHIATRIC-DIAGNOSIS

  • 1 January 1978
    • journal article
    • research article
    • Vol. 13  (2) , 265-281
Abstract
An empirical evaluation of the major clustering approaches on psychiatric diagnostic data was attempted. Experienced psychiatrists, using 17 psychopathological variables, developed 88 archetypal psychiatric patients to represent 4 diagnostic categories (manic-depressive depressed, manic-depressive manic, simple schizophrenic and paranoid schizophrenic). Ten computerized methods representative of the major clustering approaches and using various measures of similarity between patients were applied to this data set to develop de novo patient grouping. Evaluative criteria included the concordance of clustering output to the structure of the original data, and clustering replicability. Considerable differences were obtained among clustering methods. The best-ranked procedures were nearest centroid sorting methods and complete and centroid linkage hierarchical methods. The overall poorest rankings were obtained for multivariate normal mixture analysis and facial representation of multidimensional points. Further evaluation of the cluster analytic methods on real biological and psychosocial data sets yielded similar rankings.

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