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» Algorithm Visualization in Teaching Spatial Data Algorithms
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ICDM
2010
IEEE
119views Data Mining» more  ICDM 2010»
14 years 11 months ago
Visually Controllable Data Mining Methods
A large number of data mining methods are, as such, not applicable to fast, intuitive, and interactive use. Thus, there is a need for visually controllable data mining methods. Suc...
Kai Puolamäki, Panagiotis Papapetrou, Jefrey ...
120
Voted
BTW
1999
Springer
157views Database» more  BTW 1999»
15 years 6 months ago
Database Primitives for Spatial Data Mining
Abstract: Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a singl...
Martin Ester, Stefan Grundlach, Hans-Peter Kriegel...
VVS
1998
IEEE
91views Visualization» more  VVS 1998»
15 years 6 months ago
Design of Accurate and Smooth Filters for Function and Derivative Reconstruction
The correct choice of function and derivative reconstruction filters is paramount to obtaining highly accurate renderings. Most filter choices are limited to a set of commonly use...
Torsten Möller, Klaus Mueller, Yair Kurzion, ...
106
Voted
IGARSS
2010
14 years 11 months ago
Adapting the sir algorithm to ASCAT
Scatterometers have been launched primarily to measure ocean winds. The value of scatterometer data is increased by application of the SIR (Scatterometer Image Reconstruction) alg...
Richard D. Lindsley, David G. Long
124
Voted
TVCG
2002
99views more  TVCG 2002»
15 years 1 months ago
Lagrangian-Eulerian Advection of Noise and Dye Textures for Unsteady Flow Visualization
A new hybrid scheme (LEA) that combines the advantages of Eulerian and Lagrangian frameworks is applied to the visualization of dense representations of time-dependent vector field...
Bruno Jobard, Gordon Erlebacher, M. Yousuff Hussai...