最小生成树用于基因表示数据的聚类算法

在生物学研究中，需要对植物和动物分类，对基因进行分类，以获得对种群固有结构的认识．使用聚类分析方法，有效地鉴别基因表示数据的模式，将它们分组成为由类似对象组成的多个类，对研究基因的结构、功能以及不同种类基因之间的关系都具有重要意义．将图论的最小生成树理论引入分子生物学中基因表示数据的聚类分析方法，设计了生成树的表示和基于最小生成树的聚类算法，证明了该方法对于一些准则函数能够产生全局最优簇，并根据实验结果对算法进行了讨论和评价．

An Algorithm for Clustering Gene Expression Data Using Minimum Spanning Trees

Genes are divided up for computing and understanding the categories of animals and plants and for getting the knowledge about their connatural structures in the research of the biology It is important that using a clustering analysis method, modes of gene expression data are effectively recognized and they are classified as clusters which are formed by similar objects for studying their structure and function, and the relationship between different species of genes Minimum spanning trees, a graph theoretic approach, is used in clustering gene expression data of molecular biology Expression with spanning trees and clustering analysis method based on minimum spanning trees are designed It is proved that optimum clusters can be obtained by using some rule functions Discussion and evaluation are shown for the algorithm, according to the results of the experiments