Visual Data Mining

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dc.contributor.authorKeim, Daniel A.en_US
dc.contributor.authorMüller, Wolfgangen_US
dc.contributor.authorSchumann, Heidrunen_US
dc.date.accessioned2015-11-12T07:17:20Z
dc.date.available2015-11-12T07:17:20Z
dc.date.issued2002en_US
dc.description.abstractNever 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.en_US
dc.description.seriesinformationEurographics 2002 - STARsen_US
dc.identifier.issn1017-4656en_US
dc.identifier.urihttps://doi.org/10.2312/egst.20021052en_US
dc.publisherEurographics Associationen_US
dc.titleVisual Data Miningen_US
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