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130
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BMCBI
2011
14 years 4 months ago
MINE: Module Identification in NEtworks
Background: Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of function...
Kahn Rhrissorrakrai, Kristin C. Gunsalus
75
Voted
NAACL
2003
15 years 2 months ago
Automatically Discovering Word Senses
We will demonstrate the output of a distributional clustering algorithm called Clustering by Committee that automatically discovers
Patrick Pantel, Dekang Lin
74
Voted
BMCBI
2006
90views more  BMCBI 2006»
15 years 20 days ago
Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data
Background: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated...
Tricia A. Thornton-Wells, Jason H. Moore, Jonathan...
94
Voted
IBPRIA
2003
Springer
15 years 5 months ago
Incrementally Assessing Cluster Tendencies with a Maximum Variance Cluster Algorithm
A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the ...
Krzysztof Rzadca, Francesc J. Ferri
100
Voted
GECCO
2004
Springer
15 years 6 months ago
Improving the Performance of a Genetic Algorithm Using a Variable-Reordering Algorithm
Genetic algorithms have been successfully applied to many difficult problems but there have been some disappointing results as well. In these cases the choice of the internal repre...
Eduardo Rodriguez-Tello, Jose Torres-Jimenez