Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
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...
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...
“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
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...