title: A hybrid approach to clustering biomedical data.
abstract: Cluster analysis is often used in an attempt to determine the number and characteristics of patterns present in vectors of biomedical response parameters. An iterative clustering procedure based upon the combined supervised and unsupervised learning algorithm of Pao is described. The present paper illustrates this procedure using growth curves of indices of family functioning in adaptation to pediatric chronic illness. This clustering procedure is based upon neural network approaches to supervised (discriminant analysis) and unsupervised (cluster analysis) learning and is similar to fuzzy set algorithms developed to assess the degree of relatedness among a number of discrete units.
authors.affiliation: Department of Psychology University of Missouri-Columbia 65211, USA.
authors.forename: J F
authors.initials: JF
authors.lastname: Thayer
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer