Pairwise Data Clustering Accompanied by Validation and Visualisation

Pairwise proximities are often the starting point for finding clusters by applying cluster analysis techniques. We refer to this approach as pairwise data clustering (Mucha HJ (2009) ClusCorr98 for Excel 2007: clustering, multivariate visualization, and validation. In: Mucha HJ, Ritter G (eds) Classification and clustering: models, software and applications. Report 26, WIAS, Berlin, pp 14–40). A well known example is Gaussian model-based cluster analysis of observations in its simplest settings: the sum of squares and logarithmic sum of squares method. These simple methods can become more general by weighting the observations. By doing so, for instance, clustering the rows and columns of a contingency table will be performed based on pairwise chi-square distances. Finding the appropriate number of clusters is the ultimate aim of the proposed built-in validation techniques. They verify the results of the two most important families of methods, hierarchical and partitional clustering. Pairwise clustering should be accompanied by multivariate graphics such as heatmaps and plot-dendrograms.