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» Comparing Massive High-Dimensional Data Sets
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ICDM
2005
IEEE
163views Data Mining» more  ICDM 2005»
14 years 28 days ago
Efficient Text Classification by Weighted Proximal SVM
In this paper, we present an algorithm that can classify large-scale text data with high classification quality and fast training speed. Our method is based on a novel extension o...
Dong Zhuang, Benyu Zhang, Qiang Yang, Jun Yan, Zhe...
DATAMINE
2007
101views more  DATAMINE 2007»
13 years 7 months ago
Using metarules to organize and group discovered association rules
The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all ...
Abdelaziz Berrado, George C. Runger
SDM
2008
SIAM
256views Data Mining» more  SDM 2008»
13 years 8 months ago
Graph Mining with Variational Dirichlet Process Mixture Models
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda, Kenichi Kurihara
ACST
2006
13 years 8 months ago
Distributed hierarchical document clustering
This paper investigates the applicability of distributed clustering technique, called RACHET [1], to organize large sets of distributed text data. Although the authors of RACHET c...
Debzani Deb, M. Muztaba Fuad, Rafal A. Angryk
BMVC
2001
13 years 9 months ago
Recognition and retrieval via histogram trees
This paper explores a new method for analysing and comparing image histograms. The technique amounts to a novel way of backprojecting an image into one with fewer, statistically s...
Stuart Gibson, Richard Harvey