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KDD
2009
ACM
611views Data Mining» more  KDD 2009»
16 years 1 months ago
Fast approximate spectral clustering
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Donghui Yan, Ling Huang, Michael I. Jordan
BMCBI
2010
164views more  BMCBI 2010»
14 years 11 months ago
Merged consensus clustering to assess and improve class discovery with microarray data
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
CGF
1999
79views more  CGF 1999»
15 years 1 months ago
A Practical Analysis of Clustering Strategies for Hierarchical Radiosity
The calculation of radiant energy balance in complex scenes has been made possible by hierarchical radiosity methods based on clustering mechanisms. Although clustering offers an ...
Jean-Marc Hasenfratz, Cyrille Damez, Franço...
AAIM
2009
Springer
118views Algorithms» more  AAIM 2009»
15 years 8 months ago
A More Relaxed Model for Graph-Based Data Clustering: s-Plex Editing
We introduce the s-Plex Editing problem generalizing the well-studied Cluster Editing problem, both being NP-hard and both being motivated by graph-based data clustering. Instead o...
Jiong Guo, Christian Komusiewicz, Rolf Niedermeier...
EMNLP
2010
14 years 11 months ago
Clustering-Based Stratified Seed Sampling for Semi-Supervised Relation Classification
Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clusteri...
Longhua Qian, Guodong Zhou