SIMULTANEOUS CLUSTERING OF CASES AND VARIANCES

Publisher Summary The results of clustering cases depend on the variables used for analysis and, vice verse. The results of clustering variables depend on the cases used, whether those results are obtained by the application of automated classification techniques such as the k-means method or by the visual inspection of the output of ordination techniques, such as principal component analysis. It is a usual practice for consultants of data analysis to recommend their clients to reanalyze the data by deleting or sub-setting cases and/or variables to verify the stability and generality of the results of the analysis. The remedy for the problems in selecting cases and/or variables to be used in classification is to classify all cases and all variables simultaneously. A number of block clustering methods are proposed for the simultaneous clustering. Clustering is characterized as a method of increasing the goodness of fit of each model or decreasing the number of parameters of a well-fitted model through two operations—localization and merging, respectively.