Multivariate visualization using metric scaling
Pak Chung Wong, R. Daniel Bergeron · 1997
We present an efficient visualization approach to support multivariate data exploration through a simple but effective low dimensional data overview based on metric scaling. A multivariate dataset is first transformed into a set of dissimilarities between all pairs of data records. A graph configuration algorithm based on principal components is then used to determine the display coordinates of the data records in the low dimensional data overview. This overview provides a graphical summary of the multivariate data with reduced data dimensions, reduced data size, and additional data semantics. It can be used to enhance multidimensional data brushing, or to arrange the layout of other conventional multivariate visualization techniques. Real life data is used to demonstrate our approach. 1 Introduction We present an efficient visualization approach to support multivariate data exploration through a simple but effective low dimensional data overview based on metric scaling [4, 5]. This o...