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» Feature Extraction for Massive Data Mining
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IDEAL
2009
Springer
14 years 1 months ago
Supervised Feature Extraction Using Hilbert-Schmidt Norms
We propose a novel, supervised feature extraction procedure, based on an unbiased estimator of the Hilbert-Schmidt independence criterion (HSIC). The proposed procedure can be dire...
Povilas Daniusis, Pranas Vaitkus
SDM
2008
SIAM
133views Data Mining» more  SDM 2008»
13 years 8 months ago
A RELIEF Based Feature Extraction Algorithm
RELIEF is considered one of the most successful algorithms for assessing the quality of features due to its simplicity and effectiveness. It has been recently proved that RELIEF i...
Yijun Sun, Dapeng Wu
SIGKDD
2000
237views more  SIGKDD 2000»
13 years 6 months ago
The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation
Advances in data collection and storage have allowed organizations to create massive, complex and heterogeneous databases, which have stymied traditional methods of data analysis....
Stephen D. Bay, Dennis F. Kibler, Michael J. Pazza...
ICDM
2009
IEEE
198views Data Mining» more  ICDM 2009»
14 years 1 months ago
Information Extraction for Clinical Data Mining: A Mammography Case Study
Abstract—Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) ...
Houssam Nassif, Ryan Woods, Elizabeth S. Burnside,...
KDD
1999
ACM
147views Data Mining» more  KDD 1999»
13 years 11 months ago
Text Mining: Finding Nuggets in Mountains of Textual Data
Text mining appliesthe sameanalytical functions of datamining to the domainof textual information, relying on sophisticatedtext analysis techniques that distill information from f...
Jochen Dörre, Peter Gerstl, Roland Seiffert