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November 11, 2011
Comprehending data in spaces with dimensions higher than three has been one of main challenges in visualization research. Among many techniques proposed for exploratory visualization of multidimensional data, parallel coordinates scheme, which represents an N-dimensional data tuples as one polyline crossing parallel axes, has been widely applied. In this talk we will discuss our recent efforts on improving the efficiency and effectiveness of parallel coordinates with the introduction of new interaction methods and visual representations, including the integration of point presentation in parallel coordinates and using local clustering operation for data clustering. We will also demonstrate several application cases of high dimension data visualization on various domains, such as climate simulation, traffic and seismic research.

Xiaoru Yuan is a faculty member in the School of Electronics Engineering and Computer Science at Peking University. He received Bachelor degrees in chemistry and law from Peking University, China, in 1997 and 1998, respectively. He received the Ph.D. degree in computer science in 2006, from the University of Minnesota at Twin Cities. His primary research interests are in the field of visualization with emphasis on high dimensional data visualization, high performance rendering and visualization for large data, and novel visualization user interface. His co-authored work on high dynamic range volume visualization received Best Application Paper Award at the IEEE Visualization 2005 conference. For more information, visit


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