Mike Sips
Mike Sips
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TitleCited byYear
Visual analytics
PC Wong, J Thomas
IEEE Computer Graphics and Applications, 20-21, 2004
2902004
Selecting good views of high‐dimensional data using class consistency
M Sips, B Neubert, JP Lewis, P Hanrahan
Computer Graphics Forum 28 (3), 831-838, 2009
1542009
Recmap: Rectangular map approximations
R Heilmann, DA Keim, C Panse, M Sips
IEEE Symposium on Information Visualization, 33-40, 2004
1072004
Visual data mining in large geospatial point sets
DA Keim, C Panse, M Sips, SC North
IEEE Computer Graphics and Applications 24 (5), 36-44, 2004
982004
CircleView: a new approach for visualizing time-related multidimensional data sets
DA Keim, J Schneidewind, M Sips
Proceedings of the working conference on Advanced visual interfaces, 179-182, 2004
912004
Information visualization: Scope, techniques and opportunities for geovisualization
DA Keim
Exploring geovisualization, 21-52, 2005
882005
Pixel based visual data mining of geo-spatial data
DA Keim, C Panse, M Sips, SC North
Computers & Graphics 28 (3), 327-344, 2004
742004
Pixnostics: Towards measuring the value of visualization
J Schneidewind, M Sips, DA Keim
2006 IEEE Symposium On Visual Analytics Science And Technology, 199-206, 2006
542006
Visual data mining techniques
DA Keim, MO Ward
502002
Visualization of geo-spatial point sets via global shape transformation and local pixel placement
C Panse, M Sips, D Keim, S North
IEEE Transactions on Visualization and Computer Graphics 12 (5), 749-756, 2006
442006
Perceptually driven visibility optimization for categorical data visualization
S Lee, M Sips, HP Seidel
IEEE Transactions on visualization and computer graphics 19 (10), 1746-1757, 2012
362012
Pixelmaps: A new visual data mining approach for analyzing large spatial data sets
DA Keim, C Panse, M Sips, SC North
Third IEEE International Conference on Data Mining, 565-568, 2003
362003
Pixelmaps: A new visual data mining approach for analyzing large spatial data sets
DA Keim, C Panse, M Sips, SC North
Third IEEE International Conference on Data Mining, 565-568, 2003
362003
A visual analytics approach to multiscale exploration of environmental time series
M Sips, P Köthur, A Unger, HC Hege, D Dransch
IEEE Transactions on Visualization and Computer Graphics 18 (12), 2899-2907, 2012
332012
Pushing the limit in visual data exploration: Techniques and applications
DA Keim, C Panse, J Schneidewind, M Sips, MC Hao, U Dayal
Annual Conference on Artificial Intelligence, 37-51, 2003
292003
Visual analytics for comparison of ocean model output with reference data: Detecting and analyzing geophysical processes using clustering ensembles
P Köthur, M Sips, H Dobslaw, D Dransch
IEEE Transactions on Visualization and Computer Graphics 20 (12), 1893-1902, 2014
262014
Scalable pixel based visual data exploration
DA Keim, J Schneidewind, M Sips
Visual Information Expert Workshop, 12-24, 2006
232006
Fp-viz: Visual frequent pattern mining
DA Keim, J Schneidewind, M Sips
InfoVis, 2005
222005
Interactive visual summaries for detection and assessment of spatiotemporal patterns in geospatial time series
P Köthur, M Sips, A Unger, J Kuhlmann, D Dransch
Information Visualization 13 (3), 283-298, 2014
212014
Visual Analytics for Correlation‐Based Comparison of Time Series Ensembles
P Köthur, C Witt, M Sips, N Marwan, S Schinkel, D Dransch
Computer Graphics Forum 34 (3), 411-420, 2015
192015
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