Visual Data Mining

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S3_VisualDataMining_Mueller.pdf (1.77 MB)
Date
2002
Authors
Keim, Daniel A.
Müller, Wolfgang
Schumann, Heidrun
Journal Title
Journal ISSN
Volume Title
Publisher
Eurographics Association
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 has become 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 that have been developed over the last two decades to support the exploration of large data sets. In this star report, we provide an overview of information visualization and visual data mining techniques, and illustrate them using a few examples.
Description

        
@inproceedings{
:10.2312/egst.20021052
,
booktitle = {
Eurographics 2002 - STARs
},
editor = {},
title = {{
Visual Data Mining
}},
author = {
Keim, Daniel A.
 and 
Müller, Wolfgang
 and 
Schumann, Heidrun
},
year = {
2002
},
publisher = {
Eurographics Association
},
ISSN = {
1017-4656
},
ISBN = {},
DOI = {
/10.2312/egst.20021052
}
}
Citation
URI
https://doi.org/10.2312/egst.20021052
Collections
STARs