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» Maximum normalized spacing for efficient visual clustering
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KDD
2003
ACM
195views Data Mining» more  KDD 2003»
15 years 10 months ago
Visualizing changes in the structure of data for exploratory feature selection
Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power ava...
Elias Pampalk, Werner Goebl, Gerhard Widmer
3DPVT
2004
IEEE
115views Visualization» more  3DPVT 2004»
15 years 1 months ago
Estimating Curvatures and Their Derivatives on Triangle Meshes
The computation of curvature and other differential properties of surfaces is essential for many techniques in analysis and rendering. We present a finite-differences approach for...
Szymon Rusinkiewicz
VIS
2007
IEEE
169views Visualization» more  VIS 2007»
15 years 10 months ago
Visual Verification and Analysis of Cluster Detection for Molecular Dynamics
A current research topic in molecular thermodynamics is the condensation of vapor to liquid and the investigation of this process at the molecular level. Condensation is found in m...
Sebastian Grottel, Guido Reina, Jadran Vrabec, ...
VIS
2008
IEEE
204views Visualization» more  VIS 2008»
15 years 10 months ago
Surface Extraction from Multi-field Particle Volume Data Using Multi-dimensional Cluster Visualization
Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the enti...
Lars Linsen, Tran Van Long, Paul Rosenthal, Ste...
ICML
2008
IEEE
15 years 10 months ago
Accurate max-margin training for structured output spaces
Tsochantaridis et al. (2005) proposed two formulations for maximum margin training of structured spaces: margin scaling and slack scaling. While margin scaling has been extensivel...
Sunita Sarawagi, Rahul Gupta