In: G. Della Riccia, R. Kruse, H.-J. Lenz(eds.): 'Learning, networks and statistics', ISSEK-96 Workshop, Udine, Italy, September 1996 Series on CISM Courses and Lectures no. 382, Springer, Wien - New York 1997, 67-85.
Kohonen maps are often used for visualizing high-dimensional feature vectors in low-dimensional space. This approach is often recommended for supporting the clustering of data. In this paper an alternative approach is proposed which is more in the lines multivariate statistics and provides a simultaneous visualization and clustering of data. This approach combines projection and embedding methods (such as principal components or multidimensional scaling) with clustering criteria and corresponding optimization algorithms. Four distinct methods are proposed: projection pursuit clustering for quantitative data vectors, two MDS clustering methods for dissimilarity data (either with or without a representation of classes) and a group difference scaling method (known from literature).