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Iterative clustering analysis for grouping missing data in gene expression profiles

Authors
Kim, Dae-WonKang, B.Y.
Issue Date
Apr-2006
Publisher
SPRINGER-VERLAG BERLIN
Citation
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, v.3918, pp 129 - 138
Pages
10
Journal Title
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS
Volume
3918
Start Page
129
End Page
138
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40651
DOI
10.1007/11731139_17
ISSN
0302-9743
1611-3349
Abstract
Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Because a clustering method requires a complete data matrix as an input, we must estimate the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approach is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent process of clustering. Badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process.
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Kim, Dae-Won
소프트웨어대학 (소프트웨어학부)
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