research-article

Information Visualization and Visual Data Mining

Author:

Daniel A. KeimAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 8, Issue 1

Pages 1 - 8

https://doi.org/10.1109/2945.981847

Published: 01 January 2002 Publication History

Abstract

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

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Index Terms

Information Visualization and Visual Data Mining

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 8, Issue 1

January 2002

91 pages

ISSN:1077-2626

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Copyright © Copyright © 2002 IEEE. All Rights Reserved.

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IEEE Educational Activities Department

United States

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Published: 01 January 2002

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Heilala VLehesvuori SHämäläinen RKärkkäinen THong JPark J(2024)Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze DataProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635929(3-10)Online publication date: 8-Apr-2024
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