His primary areas of investigation include Data visualization, Visualization, Data mining, Visual analytics and Data science. The various areas that Daniel A. Keim examines in his Data visualization study include Data modeling, Creative visualization, Class, Computer graphics and Information retrieval. His Visualization study incorporates themes from Pixel and Time series.
His Data mining study combines topics in areas such as Context, Algorithm, Database and Cluster analysis. His work deals with themes such as Interactive visualization, Information visualization and Exploratory data analysis, which intersect with Visual analytics. His work on Analytics as part of general Data science study is frequently linked to Cultural analytics, bridging the gap between disciplines.
Daniel A. Keim mainly investigates Visualization, Visual analytics, Data mining, Data science and Data visualization. He works mostly in the field of Visualization, limiting it down to concerns involving Information retrieval and, occasionally, Nearest neighbor search. His research in the fields of Interactive visual analysis overlaps with other disciplines such as Cultural analytics.
In his study, which falls under the umbrella issue of Data mining, Query by Example is strongly linked to Database. His Data science research includes themes of Field and Knowledge extraction. His Data visualization study frequently links to related topics such as Data modeling.
His primary areas of study are Visual analytics, Visualization, Artificial intelligence, Data science and Data mining. As part of one scientific family, Daniel A. Keim deals mainly with the area of Visual analytics, narrowing it down to issues related to the Analytics, and often Interactive visual analysis, Cluster analysis and Data stream mining. His Visualization research incorporates elements of User interface and Human–computer interaction.
His Artificial intelligence research is multidisciplinary, incorporating elements of Natural language processing, Computer vision, Machine learning, Scatter plot and Pattern recognition. His Data science study combines topics from a wide range of disciplines, such as Domain, Field, Perspective and Social media. Daniel A. Keim studied Data mining and Subspace topology that intersect with Curse of dimensionality.
His scientific interests lie mostly in Visual analytics, Visualization, Artificial intelligence, Data science and Data visualization. The study incorporates disciplines such as Data modeling, Analytics, Computer graphics and Workflow in addition to Visual analytics. His Visualization research is under the purview of Data mining.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Natural language processing. His Data science research integrates issues from Domain, Field and Electric power. His Data visualization research incorporates themes from Text mining, Relational database and Dimensionality reduction.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Information visualization and visual data mining
D.A. Keim.
IEEE Transactions on Visualization and Computer Graphics (2002)
The X-tree: an index structure for high-dimensional data
Stefan Berchtold;Daniel A. Keim;Hans-Peter Kriegel.
very large data bases (2001)
An efficient approach to clustering in large multimedia databases with noise
Alexander Hinneburg;Daniel A. Keim.
knowledge discovery and data mining (1998)
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
Charu C. Aggarwal;Alexander Hinneburg;Daniel A. Keim.
international conference on database theory (2001)
On the surprising behavior of distance metrics in high dimensional space
Charu C. Aggarwal;Alexander Hinneburg;Daniel A. Keim.
Lecture Notes in Computer Science (2001)
Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases
Christian Böhm;Stefan Berchtold;Daniel A. Keim.
ACM Computing Surveys (2001)
Visual Analytics: Definition, Process, and Challenges
Daniel Keim;Gennady Andrienko;Jean-Daniel Fekete;Carsten Görg.
Information Visualization (2008)
Challenges in Visual Data Analysis
D.A. Keim;F. Mansmann;J. Schneidewind;H. Ziegler.
conference on information visualization (2006)
Mastering the information age : solving problems with visual analytics
Daniel Keim;Jörn Kohlhammer;Geoffrey Ellis;Florian Mansmann.
(2010)
What Is the Nearest Neighbor in High Dimensional Spaces
Alexander Hinneburg;Charu C. Aggarwal;Daniel A. Keim.
very large data bases (2000)
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Hewlett-Packard (United States)
Ludwig-Maximilians-Universität München
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Fraunhofer Institute for Intelligent Analysis and Information Systems
Fraunhofer Society
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Ludwig-Maximilians-Universität München
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Hewlett-Packard (United States)
Fraunhofer Institute for Intelligent Analysis and Information Systems
Publications: 95
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