Density-based Imputation Method for Fuzzy Cluster Analysis of Gene Expression Microarray Data
نویسندگان
چکیده
Fuzzy clustering has been widely used for analysis of gene expression microarray data. However, most fuzzy clustering algorithms require complete datasets and, because of technical limitations, most microarray datasets have missing values. To address this problem, we present a new algorithm where genes are clustered using the Fuzzy C-Means algorithm (FCM). The fuzzy partition obtained is then used to create a density-based fuzzy partition which is used with the FCM fuzzy partition to estimate the missing values in the dataset. We show that our method outperforms five popular imputation algorithms on both artificial and real datasets. AvailabilityThe test datasets and the software are available online at http://ouray.ucdenver.edu/~tnle/fzdbi Keywordsmicroarray data; missing value estimation; fuzzy c-means; cluster density;
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