Plotviz
A cross-platform tool for visualizing large and high-dimensional data
PlotViz is a 3D data point browser that visualizes large volume of 2- or 3-dimensional data as points in a virtual space on a computer screen and enable users to explore the virtual space interactively.
Introduction
Large-scale high dimensional data visualization is highly valuable for scientific discovery in many fields of data mining and information retrieval. PlotViz is a 3D data point browser that visualizes large volume of 2- or 3-dimensional data as points in a virtual space on a computer screen and enable users to explore the virtual space interactively. PlotViz was initially designed to consume outputs of dimension reduction algorithms for visualizing high-dimensional data in a lower-dimensional space, such as Multi-dimensional Scaling (MDS) and Generative Topographic Mapping (GTM). Used together with such dimension reduction algorithms, PlotViz can help users to discover intrinsic structures of high-dimensional data and browse large volumes of data points interactively and efficiently in a virtual 3D space.
Screenshots
Publications
Jong Youl Choi, Seung-Hee Bae, Judy Qiu, Geoffrey Fox, Bin Chen, and David Wild, "Browsing Large Scale Cheminformatics Data with Dimension Reduction," Proceedings of Emerging Computational Methods for the Life Sciences Workshop of ACM HPDC 2010 conference, Chicago, Illinois, June 20-25, 2010.
Seung-Hee Bae, Jong Youl Choi, Judy Qiu, Geoffrey Fox, "Dimension Reduction and Visualization of Large High-dimensional Data via Interpolation," In the Proceedings of ACM HPDC 2010 conference, Chicago, Illinois, June 20-25, 2010
Jong Youl Choi, Judy Qiu, Marlon Pierce, Geoffrey Fox, "Generative Topographic Mapping by Deterministic Annealing," In the Proceedings of the 10th International conference on Computational Science and Engineering (ICCS 2010), May 31 - Jun 2, 2010. Amsterdam, The Netherlands.
Jong Youl Choi, Seung-Hee Bae, Xiaohong Qiu and Geoffrey Fox, "High Performance Dimension Reduction and Visualization for Large High-dimensional Data Analysis," In the Proceedings of the The 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2010), May 17-20, 2010, Melbourne, Australia.