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» Missing Values Imputation for a Clustering Genetic Algorithm
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RSCTC
2004
Springer
148views Fuzzy Logic» more  RSCTC 2004»
13 years 10 months ago
Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method
In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We comb...
Dan Li, Jitender S. Deogun, William Spaulding, Bil...
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
13 years 7 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes
BMCBI
2008
193views more  BMCBI 2008»
13 years 5 months ago
Missing value imputation for microarray gene expression data using histone acetylation information
Background: It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile ...
Qian Xiang, Xianhua Dai, Yangyang Deng, Caisheng H...
BMCBI
2010
110views more  BMCBI 2010»
13 years 5 months ago
Missing value imputation for epistatic MAPs
Background: Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The dat...
Colm Ryan, Derek Greene, Gerard Cagney, Padraig Cu...
PKDD
1999
Springer
272views Data Mining» more  PKDD 1999»
13 years 9 months ago
Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation
Abstract. In many applications of data mining a - sometimes considerable - part of the data values is missing. This may occur because the data values were simply never entered into...
A. J. Feelders