Information theory in scientific visualization

Authors

Chaoli Wang, Michigan Technological University
Han-Wei Shen, The Ohio State University

Document Type

Article

Publication Date

1-21-2011

Department

Department of Computer Science

Abstract

In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.

Publisher's Statement

© 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/.) Publisher’s version of record: https://doi.org/10.3390/e13010254

Publication Title

Entropy

Recommended Citation

Wang, C., & Shen, H. (2011). Information theory in scientific visualization. Entropy, 13(1), 254-273. http://doi.org/10.3390/e13010254
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1991

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.

Version

Publisher's PDF