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KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 9 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
BIODATAMINING
2008
178views more  BIODATAMINING 2008»
13 years 9 months ago
Clustering-based approaches to SAGE data mining
Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical appli...
Haiying Wang, Huiru Zheng, Francisco Azuaje
WECWIS
2002
IEEE
131views ECommerce» more  WECWIS 2002»
14 years 2 months ago
Mining Client-Side Activity for Personalization
“Garbage in. garbage out” is a well-known phrase in computer analysis, and one that comes to mind when mining Web data to draw conclusions about Web users. The challenge is th...
Kurt D. Fenstermacher, Mark Ginsburg
DEXAW
2005
IEEE
133views Database» more  DEXAW 2005»
13 years 11 months ago
Inductive Databases: Towards a New Generation of Databases for Knowledge Discovery
Data mining applications are typically used in the decision making process. The Knowledge Discovery Process (KDD process for short) is a typical iterative process, in which not on...
Rosa Meo
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
14 years 9 months ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher