MIXED DATA TYPE AND TOPOLOGICAL CLASSIFICATION(Multidimensional Data Analysis)

The analysis of high dimensional data containing both continuous and discrete variables is a standard task in applied biometry. Statistical software packages offer classification procedures concerning continuous variables basing on a suitable coordinate transformation of the finite dimensional real data space. Such transformations are of algebraic-topologic nature. Statistical interpretations require that additional suppositions are fulfilled on probability distributions. For discrete variables, nonprobabilistic classification procedures are available from certain metrics. We discuss a classification procedure for mixed binary and continuous type data. TANIMOTO and MAHALANOBIS distance are combined for this purpose. The computations are carried out in a SAS environment. For example, the method is applied to data of alcoholics in traffic.