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» Practical Preference Relations for Large Data Sets
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CIDR
2003
218views Algorithms» more  CIDR 2003»
13 years 9 months ago
Indexing Large Trajectory Data Sets With SETI
With the rapid increase in the use of inexpensive, location-aware sensors in a variety of new applications, large amounts of time-sequenced location data will soon be accumulated....
V. Prasad Chakka, Adam Everspaugh, Jignesh M. Pate...
VISUALIZATION
2005
IEEE
14 years 1 months ago
Query-Driven Visualization of Large Data Sets
We present a practical and general-purpose approach to large and complex visual data analysis where visualization processing, rendering and subsequent human interpretation is cons...
Kurt Stockinger, John Shalf, Kesheng Wu, E. Wes Be...
VISUALIZATION
2002
IEEE
14 years 1 months ago
Interactive Rendering of Large Volume Data Sets
We present a new algorithm for rendering very large volume data sets at interactive framerates on standard PC hardware. The algorithm accepts scalar data sampled on a regular grid...
Stefan Guthe, Michael Wand, Julius Gonser, Wolfgan...
DATAMINE
2006
166views more  DATAMINE 2006»
13 years 8 months ago
Accelerated EM-based clustering of large data sets
Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms l...
Jakob J. Verbeek, Jan Nunnink, Nikos A. Vlassis
SIGMOD
2000
ACM
173views Database» more  SIGMOD 2000»
13 years 12 months ago
Efficient Algorithms for Mining Outliers from Large Data Sets
In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis o...
Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim