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» Finding Groups in Data: Cluster Analysis with Ants
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BMCBI
2008
115views more  BMCBI 2008»
13 years 6 months ago
Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient
Background: Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson corre...
Jianchao Yao, Chunqi Chang, Mari L. Salmi, Yeung S...
KDD
2002
ACM
147views Data Mining» more  KDD 2002»
14 years 7 months ago
Visualized Classification of Multiple Sample Types
The goal of the knowledge discovery and data mining is to extract the useful knowledge from the given data. Visualization enables us to find structures, features, patterns, and re...
Li Zhang, Aidong Zhang, Murali Ramanathan
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
13 years 11 months ago
OPTICS-OF: Identifying Local Outliers
: For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commer...
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. ...
KDD
2002
ACM
173views Data Mining» more  KDD 2002»
14 years 7 months ago
LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition
Web Usage Mining enables new understanding of user goals on the Web. This understanding has broad applications, and traditional mining techniques such as association rules have bee...
Ed Huai-hsin Chi, Adam Rosien, Jeffrey Heer
BMCBI
2007
173views more  BMCBI 2007»
13 years 6 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...