John Tukey (EDA, 1977) lectured us “It is important to understand what you CAN DO before you learn to measure how WELL you seem to have DONE it.” Data analysts and statistics practitioners nowadays are facing difficulties in understanding higher and higher dimensional data with more and more complex nature while conventional graphics/visualization tools do not answer the needs. It is statisticians' responsibility for coming up with graphics/visualization environment that can help users really understand what one CAN DO for complex data generated from modern techniques and sophisticated experiments.
In this talk I'll summarize our works on matrix visualization for interpreting statistics and statistical approach for implementing matrix visualization. We create matrix visualization environment for conducting statistical analyses, from descriptive statistics, model fitting, inference, to diagnosing. On the other end, we also introduce statistical concepts into matrix visualization environment for visualizing more versatile and complex data structure. With these two matrix visualization procedures interact with each other we hope a good EDA solution can be achieved.
Keywords: Exploratory data analysis; Heatmap; Matrix visualization; Seriation
Biography: Chun-houh Chen earned his Ph.D. degree in mathematics from UCLA in 1992. He started his professional career as an assistant professor of statistics at The George Washington University, USA. In 1993, Dr. Chen went back to Taiwan to continue his research career at the Institute of Statistical Science, Academia Sinica. His research interests include bioinformatics/systems biology, data/information visualization, and statistical pattern recognition. Matrix visualization through Generalized Association Plots (GAP) for various types and conditions of data is his major way of data/information visualization.