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KDD
2010
ACM
222views Data Mining» more  KDD 2010»
15 years 9 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
SDM
2012
SIAM
452views Data Mining» more  SDM 2012»
13 years 9 months ago
Density-based Projected Clustering over High Dimensional Data Streams
Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately ...
Irene Ntoutsi, Arthur Zimek, Themis Palpanas, Peer...
ADMA
2005
Springer
124views Data Mining» more  ADMA 2005»
15 years 9 months ago
Finding All Frequent Patterns Starting from the Closure
Efficient discovery of frequent patterns from large databases is an active research area in data mining with broad applications in industry and deep implications in many areas of d...
Mohammad El-Hajj, Osmar R. Zaïane
KDD
2002
ACM
179views Data Mining» more  KDD 2002»
16 years 7 months ago
Combining clustering and co-training to enhance text classification using unlabelled data
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
Bhavani Raskutti, Herman L. Ferrá, Adam Kow...
PKDD
2009
Springer
174views Data Mining» more  PKDD 2009»
16 years 1 months ago
Active and Semi-supervised Data Domain Description
Data domain description techniques aim at deriving concise descriptions of objects belonging to a category of interest. For instance, the support vector domain description (SVDD) l...
Nico Görnitz, Marius Kloft, Ulf Brefeld