Publikation: Visual Data Mining Techniques
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2002
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Ward, Matthew O.
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BERTHOLD, Michael, ed. and others. Intelligent Data Analysis: An Introduction. Berlin: Springer, 2002, pp. 2-27
Zusammenfassung
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 paper, we propose a classification of information visualization and visual data mining techniques based on the data type to be visualized, the visualization technique, and the interaction technique. We illustrate the classification using a few examples, and indicate some directions for future work.
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KEIM, Daniel A., Matthew O. WARD, 2002. Visual Data Mining Techniques. In: BERTHOLD, Michael, ed. and others. Intelligent Data Analysis: An Introduction. Berlin: Springer, 2002, pp. 2-27BibTex
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<dcterms:abstract xml:lang="eng">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 paper, we propose a classification of information visualization and visual data mining techniques based on the data type to be visualized, the visualization technique, and the interaction technique. We illustrate the classification using a few examples, and indicate some directions for future work.</dcterms:abstract>
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