01639nas a2200217   4500000000100000000000100001008004100002260001200043653001500055653003100070653002000101100001500121700001900136700002000155245011400175856008000289300000700369490000600376520102500382022001401407       2020                            d  c06/202010aClustering10aClustering Quality Indexes10aGene Expression1 aHouda Fyad1 aFatiha Barigou1 aKarim Bouamrane00aAn Experimental Study on Microarray Expression Data from Plants under Salt Stress by using Clustering Methods  uhttps://www.ijimai.org/journal/sites/default/files/2020-05/ijimai_6_2_5.pdf  a100 v63 aCurrent Genome-wide advancements in Gene chips technology provide in the “Omics (genomics, proteomics and transcriptomics) research”, an opportunity to analyze the expression levels of thousand of genes across multiple experiments. In this regard, many machine learning approaches were proposed to deal with this deluge of information. Clustering methods are one of these approaches. Their process consists of grouping data (gene profiles) into homogeneous clusters using distance measurements. Various clustering techniques are
applied, but there is no consensus for the best one. In this context, a comparison of seven clustering algorithms was performed and tested against the gene expression datasets of three model plants under salt stress. These techniques are evaluated by internal and relative validity measures. It appears that the AGNES algorithm is the best one for internal validity measures for the three plant datasets. Also, K-Means profiles a trend for relative validity measures for these datasets.  a1989-1660