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» On Class Visualisation for High Dimensional Data: Exploring ...
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BMCBI
2011
12 years 11 months ago
To aggregate or not to aggregate high-dimensional classifiers
Background: High-throughput functional genomics technologies generate large amount of data with hundreds or thousands of measurements per sample. The number of sample is usually m...
Cheng-Jian Xu, Huub C. J. Hoefsloot, Age K. Smilde
CEC
2010
IEEE
13 years 8 months ago
A novel framework to elucidate core classes in a dataset
In this paper we present an original framework to extract representative groups from a dataset, and we validate it over a novel case study. The framework specifies the application ...
Daniele Soria, Jonathan M. Garibaldi
ICDM
2003
IEEE
125views Data Mining» more  ICDM 2003»
14 years 25 days ago
Clustering Item Data Sets with Association-Taxonomy Similarity
We explore in this paper the efficient clustering of item data. Different from those of the traditional data, the features of item data are known to be of high dimensionality and...
Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen
RECOMB
2001
Springer
14 years 7 months ago
Class discovery in gene expression data
Recent studies (Alizadeh et al, [1]; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computa...
Amir Ben-Dor, Nir Friedman, Zohar Yakhini
BMCBI
2010
190views more  BMCBI 2010»
13 years 7 months ago
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification alg
Background: Data generated using `omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of...
Yu Guo, Armin Graber, Robert N. McBurney, Raji Bal...