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» Dimensionality Reduction of Clustered Data Sets
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ICML
2003
IEEE
14 years 8 months ago
Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach
We investigate how random projection can best be used for clustering high dimensional data. Random projection has been shown to have promising theoretical properties. In practice,...
Xiaoli Zhang Fern, Carla E. Brodley
DEBU
1998
86views more  DEBU 1998»
13 years 7 months ago
Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results
Eui-Hong Han, George Karypis, Vipin Kumar, Bamshad...
NN
2002
Springer
144views Neural Networks» more  NN 2002»
13 years 7 months ago
Projective ART for clustering data sets in high dimensional spaces
A new neural network architecture (PART) and the resulting algorithm are proposed to
Yongqiang Cao, Jianhong Wu
WIRN
2005
Springer
14 years 27 days ago
Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
Alberto Bertoni, Giorgio Valentini
ICDE
2007
IEEE
228views Database» more  ICDE 2007»
14 years 1 months ago
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the “curse of dimensionality”. In order to speed up the retrieval efficiency, p...
Xiang Lian, Lei Chen 0002