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This paper presents a solution to the problem of how to identify the units in groups or clusters that have the greatest degree of centrality and best characterize each group. This problem frequently arises in the classification of data such as types of tumor, gene expression profiles or general biomedical data. It is particularly important in the common context that many units do not properly belong...
Clustering gene expression data is an important task in bioinformatics research and biomedical applications. In this paper, we present an effective clustering algorithm for gene expression data. The clustering algorithm is based on the analysis of data's density distribution. We propose an intersecting partition of gene expression data into the supports of data points. Density clusters are maximally...
Cluster analysis of gene expression data is one of the most useful tools for identifying biologically relevant groups of genes, however, gene expression data suffer severely from the problems of measurement noise, dimension curse, high redundancy between genes, and the functional annotation of genes is incomplete and imprecise. These properties lead to most of the traditional clustering algorithms...
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