Clive J, Woodbury M A, Siegler I C
J Med Syst. 1983 Aug;7(4):317-32. doi: 10.1007/BF01080688.
Conventional cluster analyses of patient populations are intended to assist in the identification and characterization of groups that 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. As an alternative to conventional clustering procedures, several investigators have studied the use of fuzzy classification methods. In general, these measure a patient's clinical status in terms of a real number defined on the closed unit interval, reflecting the extent or degree to which a particular grouping entity characterizes the patient. 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 both 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. The fuzzy set-theoretic-based model was found to be more sensitive to changes in subject level of functioning over time, to provide superior quantitative protrayals of patterns of aging, and to reflect properties of the aging process derived from other research.
对患者群体进行传统的聚类分析旨在帮助识别和描述可能代表特定疾病类别中的病因或病理亚型的群体。这些方法被批评对患者的细微差异不敏感，因为基于清晰集理论的分组算法中固有的聚类成员全有或全无的概念，这些细微差异可能会被掩盖。作为传统聚类程序的替代方法，一些研究人员研究了模糊分类方法的使用。一般来说，这些方法根据在闭区间[0,1]上定义的实数来衡量患者的临床状态，反映特定分组实体对患者的表征程度。本文比较并对比了基于清晰集理论和模糊集理论的聚类程序在一组描述参与衰老纵向研究的一组受试者认知和智力功能的数据上的应用。重点放在分别与聚类定义的临床解释相对应的定性和定量方面，以及分类程序对患者特征随时间变化的稳健性或敏感性。结果发现，基于模糊集理论的模型对受试者功能水平随时间的变化更敏感，能提供更优的衰老模式定量描述，并能反映从其他研究中得出的衰老过程特性。
