A Memory-Saving and Efficient Data Transformation Technique for Mixed Data Sets Visualization

Although there have been effective visualizations for simplex continuous or categorical variables, mixed data sets are still difficult to visualize, since no direct approaches are available for them. This paper presents a memory-saving and efficient data transformation technique for mixed data sets visualization,particularly details on describing the application of Correspondence Analysis to quantify categorical variables, and proposes a set of cardinality reduction strategies to reduce the numbers of variables and their values involved in computations. A series of empirical studies are carried out in a Star Coordinates-based environment to evaluate the visualization of mixed datasets. Finally it is concluded that the visualization gives a good graphical view of mixed data sets, with the data transformation technique being efficient in both time and memory.