An optimal hierarchical clustering algorithm for gene expression data
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Clustering gene expression data analysis using an improved em algorithm based on multivariate elliptical contoured mixture models
2014, OptikCitation Excerpt :Many clustering algorithms have been proposed for gene expression data analysis. The hierarchical clustering is one of the earliest algorithms applied to clustering gene expression data [6,7]. K-means clustering algorithm is used in gene expression data analysis due to its high computational performances [8,9].
SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index
2010, Pattern RecognitionCitation Excerpt :Clustering is widely used in many fields such as psychology [1], biology [2,3], pattern recognition [4], image processing [5,6] and computer security [7].
Class-Specific Correlations of Gene Expressions: Identification and Their Effects on Clustering Analyses
2008, American Journal of Human GeneticsCitation Excerpt :DNA microarray technology provides a unique tool to monitor gene-expression levels of thousands of genes simultaneously. To detect gene-transcriptional modules in microarray data, a main step is often the application of clustering analyses,3–6 which can group genes with similar expression profiles.4,7,8 In recent years, various clustering-based methods have been proposed, such as hierarchical clustering,4 K-means,9 and self-organizing map (SOM).10,11
AGGLO-Hi clustering algorithm for gene expression micro array data using proximity measures
2020, Multimedia Tools and ApplicationsA new clustering method of gene expression data based on multivariate Gaussian mixture models
2016, Signal, Image and Video ProcessingKansei clustering using fuzzy and grey relation algorithms
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