论文信息 - Clustering of gene expression data based on self-growth tree

Clustering of gene expression data based on self-growth tree

A novel approach used in gene expression data is proposed in this paper. Unlike traditional hierarchical clustering, this method has a lower computational complexity and more rational tree structure, and compared with K-means method, it is influenced less by people. It is also applied in the cell cycle data set reported by Cho, and obtains some good results.

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