Effect of data normalization on fuzzy clustering of DNA microarray data |
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Authors: | Seo Young Kim Jae Won Lee Jong Sung Bae |
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Affiliation: | (1) Research Institute for Basic Science, Chonnam National University, Gwangju, 500-757, Korea;(2) Department of Statistics, Korea University, Seoul, Korea;(3) Department of Statistics, Chonnam National University, Gwangju, 500-757, Korea |
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Abstract: | Background Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. |
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