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- W2356020937 abstract "With the rapid development of DNA microarray technology,mass gene expression data are generated.The basic starting point of clustering analysis algorithm which is one of the major technologies in analyzing gene expression data is to divide the objects into different classes according to their similarity.The selection of appropriate similarity measurement criteria is a key to obtain the effective clustering results.In this paper,the pre-treated gene data sets are adopted to conduct the clustering analysis of the different clustering algorithms under different similarity measurement criterions and achieve the evaluation of clustering results.The defects of the algorithm itself and limitation of the distance similarity measurement are the factors affecting the outcome evaluation.Correlation clustering algorithms are improved and the proportion similarity measurement criteria is proposed to obtain more effective clustering effect." @default.
- W2356020937 created "2016-06-24" @default.
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- W2356020937 date "2012-01-01" @default.
- W2356020937 modified "2023-09-24" @default.
- W2356020937 title "Application of similarity measurement in gene expression clustering analysis" @default.
- W2356020937 hasPublicationYear "2012" @default.
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