%0 Journal Article %1 Keim2002 %A Keim, Daniel A. %C Piscataway, NJ, USA %D 2002 %I IEEE Educational Activities Department %J IEEE Transactions on Visualization and Computer Graphics %K %N 1 %P 1--8 %R http://dx.doi.org/10.1109/2945.981847 %T Information Visualization and Visual Data Mining %V 8 %X Never before in history data has been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data becomes 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 is 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 issue.

@article{Keim2002, abstract = {Never before in history data has been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data becomes 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 is 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 issue.}, added-at = {2012-07-13T12:00:02.000+0200}, address = {Piscataway, NJ, USA}, author = {Keim, Daniel A.}, biburl = {https://www.bibsonomy.org/bibtex/2396d3d8e7cf3fa33e20812a25068ea34/jabreftest}, doi = {http://dx.doi.org/10.1109/2945.981847}, file = {Keim2002.pdf:2002/Keim2002.pdf:PDF}, groups = {public}, interhash = {a87e01f867ff0d4366dae6522d664e2c}, intrahash = {396d3d8e7cf3fa33e20812a25068ea34}, issn = {1077-2626}, journal = {IEEE Transactions on Visualization and Computer Graphics}, keywords = {}, number = 1, pages = {1--8}, publisher = {IEEE Educational Activities Department}, timestamp = {2012-07-13T12:00:02.000+0200}, title = {Information Visualization and Visual Data Mining}, username = {jabreftest}, volume = 8, year = 2002 }