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» Randomization in Privacy-Preserving Data Mining
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SIGMOD
1998
ACM
99views Database» more  SIGMOD 1998»
15 years 4 months ago
CURE: An Efficient Clustering Algorithm for Large Databases
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
CIKM
2009
Springer
15 years 7 months ago
Privacy-enhanced public view for social graphs
We consider the problem of releasing a limited public view of a sensitive graph which reveals at least k edges per node. We are motivated by Facebook’s public search listings, w...
Hyoungshick Kim, Joseph Bonneau
PKDD
2007
Springer
91views Data Mining» more  PKDD 2007»
15 years 6 months ago
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabel...
Sandeepkumar Satpal, Sunita Sarawagi
SAC
2006
ACM
15 years 6 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
WSDM
2009
ACM
187views Data Mining» more  WSDM 2009»
15 years 7 months ago
Speeding up algorithms on compressed web graphs
A variety of lossless compression schemes have been proposed to reduce the storage requirements of web graphs. One successful approach is virtual node compression [7], in which of...
Chinmay Karande, Kumar Chellapilla, Reid Andersen