Visualizing Multi-Dimensional Data

High dimensional data visualization is very important in data analysts since it gives a direct and natural view of data. In this paper, we propose a method to visualize large amount of high dimensional data in a 3-D space. In our method, we divide the high dimension data into several groups of lower dimensional data first. Then, we use different icons to represent different groups. Initial experiments on a real data set from oil industry have provided us very encouraging results although further improvements are needed. 1. Background information The rapid emergence of electronic data management methods has led us to the "Information Age". Powerful database systems for collecting and managing data are almost in all large and midrange companies and public organizations. Today's large databases contain huge amount of data that becoming almost impossible to manually analyze them to extract valuable information. The need for automated extraction of useful knowledge from large databases is widely recognized now, and leads to a rapidly developing market of automated analysis and discovery tools. Knowledge discovery and data mining are recent techniques to discover strategic information hidden in very large databases. Although automated discovery tools have the capability to analyze the raw data and present the extracted information to the analyst or decision maker, human insight is still very important to extract high-level information from a data set. Visualization plays an important role in making the discovered knowledge understandable and interpretable by human beings. Besides, the human eye-brain system itself still remains the best pattern recognition device known. Visualization techniques may range from simple scatter plots and histogram plots over parallel coordinates to 3D movies. Traditional visualization techniques, such as histogram, pie, tree, work well with the field of statistics, where details about data are not necessary. Nevertheless, current data analysis prefers rendering more data details and has to deal with a dataset with high dimensions. Visualization techniques like EXVIS [1] are often called glyph-based methods. Glyphs are graphical icons with visual features such as shape, orientation, color and size that are controlled by attributes in an underlying dataset. Glyph-based techniques range from representation via individual icons to the formation of texture and color patterns through the overlay of many thousands of glyphs. Initial work by Chernoff suggested the use of