Abstract
This chapter discusses the role of computer graphics in interpretation of clustering results. The utility of computer graphics has won the recognition of many researchers specializing in data analysis, and a variety of graphical representation methods have been proposed by them. From graphical representation, data analysts expect to obtain a firm grasp of the intrinsic structure of collected data by visualizing their features. The hardware environment bears closely upon free use of graphical representation as a means of data analysis. It deserves attention that the functional improvement of microcomputers and workstations has provided freedom of graphical representation and greater ease of graphics equipment operation. These graphical representation methods have some common features—(1) representing the features of multivariate data, (2) visual inspection of the relationship among variables, (3) observing data distribution, (4) projections based on data transformation and features selection, and (5) enhancement of intrinsic features in data.
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