Visualization of multidimensional data using structure preserving projection methods

Multi Dimensional Scaling is a structure preserving projection method that allows for the visualization of multidimensional data. In this paper we discuss our practical experience in using MDS as a projection method in three different application scenarios. Various reasons are given why structure preserving pro­ jection methods are useful for the analysis of multidimensional data. We discuss two visual forms (glyphs, heightfields) which can be used to represent the output of the projection methods. In this paper we discuss our practical experience in using Multi Dimensional Scaling (MDS) for the visualization of multidimensional data. We show how MDS is used to gain insight into multidimensional spaces that are represented in a table. A large class of data can be characterized by tables. Such tables can be described by a matrix of attribute variables in one dimension and the outcome of specific cases in the other. Discovery and understanding of the structure in this type of data has many applications in science and business, [1]. Here the word structure refers to geometric relationships among subsets of the data variables in the table. Examples of structure include clusters, regular patterns, outliers, distance relations, proximity of data points etc. There are many numerical and statistical techniques that can be used to an­ alyze structural information from multidimensional data tables. These tech­ niques can be used to automatically extract certain structural properties from the data. Examples of such techniques are principal component analysis (peA),