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» A Tutorial on Spectral Clustering
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BMCBI
2008
142views more  BMCBI 2008»
13 years 7 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
BMCBI
2008
144views more  BMCBI 2008»
13 years 7 months ago
WGCNA: an R package for weighted correlation network analysis
Background: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method ...
Peter Langfelder, Steve Horvath
ICML
2004
IEEE
14 years 8 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
DCC
2009
IEEE
14 years 8 months ago
Clustered Reversible-KLT for Progressive Lossy-to-Lossless 3d Image Coding
The RKLT is a lossless approximation to the KLT, and has been recently employed for progressive lossy-to-lossless coding of hyperspectral images. Both yield very good coding perfo...
Ian Blanes, Joan Serra-Sagristà
TMM
2008
112views more  TMM 2008»
13 years 7 months ago
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint
Story clustering is a critical step for news retrieval, topic mining, and summarization. Nonetheless, the task remains highly challenging owing to the fact that news topics exhibit...
Xiao Wu, Chong-Wah Ngo, Alexander G. Hauptmann