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» Using Formal Concept Analysis for Microarray Data Comparison
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WCRE
2009
IEEE
14 years 15 days ago
Domain Feature Model Recovery from Multiple Applications Using Data Access Semantics and Formal Concept Analysis
Feature models are widely employed in domainspecific software development to specify the domain requirements with commonality and variability. A feature model is usually construct...
Yiming Yang, Xin Peng, Wenyun Zhao
DAWAK
2010
Springer
13 years 6 months ago
Mining Closed Itemsets in Data Stream Using Formal Concept Analysis
Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data...
Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
CSDA
2008
128views more  CSDA 2008»
13 years 5 months ago
Assessing agreement of clustering methods with gene expression microarray data
In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face...
Xueli Liu, Sheng-Chien Lee, George Casella, Gary F...
BMCBI
2005
86views more  BMCBI 2005»
13 years 5 months ago
WebArray: an online platform for microarray data analysis
Background: Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematic...
Xiaoqin Xia, Michael McClelland, Yipeng Wang
DATESO
2004
116views Database» more  DATESO 2004»
13 years 7 months ago
Using Blind Search and Formal Concepts for Binary Factor Analysis
Binary Factor Analysis (BFA, also known as Boolean Factor Analysis) may help with understanding collections of binary data. Since we can take collections of text documents as binar...
Ales Keprt