模糊C-均值聚类分析在基因表达数据分析中的应用  

Application of Fuzzy C-means Clustering Analysis in Gene Expression Data Analysis

作　　者：陈佳妮[1] 段文英[1] 丁徽[1] 

机构地区：[1]东北林业大学,哈尔滨150040

出　　处：《森林工程》2010年第2期54-57,76,共5页Forest Engineering

摘　　要：为更好地挖掘基因表达数据、获取更多的生物学信息,近年来许多改进的传统聚类算法和新聚类算法不断涌现。模糊聚类在聚类分析中又具有广泛的意义和重要的应用价值。叙述基因表达数据的获取和表达,介绍应用在基因表达数据中的模糊C均值聚类算法以及不同的实现途径和相应的优缺点,简述聚类结果的评价问题,并对发展趋势做进一步的展望。In order to better analyze the gene expression data and obtain more biological information, many improved traditional clustering algorithms and new clustering algorithms have emerged continually in recent years. The fuzzy clustering has a widespread significance and important application value in the clustering analysis. The acquisition and expression of gene data were firstly introduced, then the fuzzy C - means clustering method applied in gene expression data and the different realized approaches were mainly discussed, as well as its advantages and disadvantages. At last the evaluation of clustering results and its development trend were briefly described.

关 键 词：DNA微阵列 基因表达数据 聚类分析 

分 类 号：O212.4[理学—概率论与数理统计]