Structural Representation of Categorical Data and Cluster Analysis Through Filters

摘要: Representation of categorical data by nominal measurement leaves the entire information intact, which is not case with widely used numerical or pseudo-numerical representation such as Likert-type scoring. This aspect first explained, and then we turn our attention to analysis nominally represented data. For a large number variables, one typically resorts dimension reduction, its necessity often greater than continuous In spite this, Nishisato S, Clavel JG (Behaviormetrika 57:15–32, 2010) proposed an approach diametrically opposite dimension-reduction approach, for they advocate use doubled hyper-space accommodate both row variables column two-way in common space. The rationale space can be vindicate validity Carroll-Green-Schaffer scaling (Carroll JD, Green PE, Schaffer CM (1986) J Mark Res 23(3):271–280). current paper will introduce simple procedure hyper-dimensional configuration data, called cluster through filters. A example presented show clear contrast between total analysis. There no doubt that preferred on two grounds: results are factual summary multidimensional configuration, practical.