Grouping Functionally Similar Genes From Microarray Data Using Rough–Fuzzy Clustering

2014 ◽  
pp. 197-224 ◽  
Author(s):  
Pradipta Maji ◽  
Sushmita Paul
Keyword(s):  
Fuzzy Clustering ◽  
Microarray Data

scholarly journals Estimation of Missing Values Using Hybrid Fuzzy Clustering Mean and Majority Vote for Microarray Data

2019 ◽  
Vol 163 ◽  
pp. 145-153
Author(s):  
Shamini Raja Kumaran ◽  
Mohd Shahizan Othman ◽  
Lizawati Mi Yusuf ◽  
Arda Yunianta
Keyword(s):  
Fuzzy Clustering ◽  
Microarray Data ◽  
Missing Values ◽  
Majority Vote

Fuzzy clustering analysis of microarray data

2008 ◽  
Vol 222 (7) ◽  
pp. 1143-1148 ◽  
Author(s):  
Lixin Han ◽  
Xiaoqin Zeng ◽  
Hong Yan
Keyword(s):  
Fuzzy Clustering ◽  
Microarray Data ◽  
Clustering Analysis ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Principal Component ◽  
Microarray Data Analysis ◽  
Clustering Method ◽  
Fuzzy Clustering Analysis ◽  
Fuzzy Clustering Method

Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.

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