Balanced layouts using the composite data-variable matrix

Numerous methods have been described that allow the visualization of the data-variable matrix. But all suffer from a common problem-visualizing the data and variable points separately which is hard for people to catch the relations in data and variables together. We describe a method that allows data and variables balanced layouts. We achieve it by combining two distance matrices typically used in isolation - the distance matrix encoding the similarities of the variables and the distance matrix encoding the similarity of the data points. The remaining two submatrices are obtained by creating a fused distance matrix - one that measures the distance of data points with respect to the variables or vice versa. We then use MDS to simultaneously optimize the placement of data points and variable points, producing a display that allows users to appreciate all three types of relationships in a single display: (1) the patterns of the collection of data items, (2) the patterns of the collection of variables, and (3) the relationships of data items with the variables and vice versa.