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» Informative sampling for large unbalanced data sets
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ICTAI
2002
IEEE
14 years 19 days ago
Data Mining for Selective Visualization of Large Spatial Datasets
Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for patte...
Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang, Ruli...
JPDC
2011
219views more  JPDC 2011»
13 years 2 months ago
BlobSeer: Next-generation data management for large scale infrastructures
As data volumes increase at a high speed in more and more application fields of science, engineering, information services, etc., the challenges posed by data-intensive computing...
Bogdan Nicolae, Gabriel Antoniu, Luc Bougé,...
NIPS
1997
13 years 9 months ago
EM Algorithms for PCA and SPCA
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large col...
Sam T. Roweis
WWW
2005
ACM
14 years 8 months ago
Sampling search-engine results
We consider the problem of efficiently sampling Web search engine query results. In turn, using a small random sample instead of the full set of results leads to efficient approxi...
Aris Anagnostopoulos, Andrei Z. Broder, David Carm...
VISUALIZATION
1999
IEEE
13 years 12 months ago
Hierarchical Parallel Coordinates for Exploration of Large Datasets
Our ability to accumulate large, complex (multivariate) data sets has far exceeded our ability to effectively process them in search of patterns, anomalies, and other interesting ...
Ying-Huey Fua, Matthew O. Ward, Elke A. Rundenstei...