FUZZY AND CRISP SET-THEORETIC BASED CLASSIFICATION OF HEALTH AND DISEASE: A QUALITATIVE AND QUANTITATIVE COMPARISON.
Conventional cluster analyses of patient populations are intended to assist in the identification and characterization of groups which may represent etiological or pathological subtypes within a particular disease class. These methods have been criticized as being insensitive to subtle patient differences, which may be masked as a result of the all or nothing concept of cluster membership intrinsic to crisp set-theoretic based grouping algorithms. This paper compares and contrasts the applications of crisp and fuzzy set-theoretic based clustering procedures to a set of data describing the cognitive and intellectual functioning of a group of subjects participating in a longitudinal study of aging. Emphasis is placed on qualitative and quantitative aspects, corresponding, respectively, to the clinical interpretation of cluster definitions, and the robustness or sensitivity of the classification procedures to changes in patient profiles over time.
