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» Efficient Discovery of Confounders in Large Data Sets
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SIGMOD
2000
ACM
173views Database» more  SIGMOD 2000»
13 years 9 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
ICML
2000
IEEE
14 years 6 months ago
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets
This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
Paul Komarek, Andrew W. Moore
KDD
2002
ACM
155views Data Mining» more  KDD 2002»
14 years 5 months ago
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Hichem Frigui
ICCS
2007
Springer
13 years 9 months ago
Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets
Abstract. Interpolating scattered data points is a problem of wide ranging interest. One of the most popular interpolation methods in geostatistics is ordinary kriging. The price f...
Nargess Memarsadeghi, David M. Mount
JDA
2008
100views more  JDA 2008»
13 years 5 months ago
Efficient sampling of random permutations
We show how to uniformly distribute data at random (not to be confounded with permutation routing) in two settings that are able to deal with massive data: coarse grained parallel...
Jens Gustedt