What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
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.
His most cited work include:
- Information visualization and visual data mining (1337 citations)
- The X-tree: an index structure for high-dimensional data (1180 citations)
- On the Surprising Behavior of Distance Metrics in High Dimensional Spaces (1174 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Visualization (36.44%)
- Visual analytics (31.88%)
- Data mining (31.88%)
What were the highlights of his more recent work (between 2013-2021)?
- Visual analytics (31.88%)
- Visualization (36.44%)
- Artificial intelligence (20.91%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- Knowledge Generation Model for Visual Analytics (183 citations)
- Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis (131 citations)
- The Role of Uncertainty, Awareness, and Trust in Visual Analytics (114 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
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.
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