Cluster validation techniques for genome expression data
Cluster validation techniques for genome expression data
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DOI：
10.1016/s0165-1684(02)00475-9
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发表时间：
2003-04-01
影响因子：
4.4
通讯作者：
Azuaje, F
中科院分区：
文献类型：
--
作者：
Bolshakova, N;Azuaje, F
已经提出了几种聚类算法来分析基因组表达数据，但用于指导基于聚类的实验的设计和评估其结果的质量的解决方案较少。集群有效性框架为预测正确的集群数量的问题提供了洞察力。本文介绍了几种用于基因表达数据分析的验证技术。提出了归一化和有效性聚集策略，以提高对相关聚类个数的预测。结果表明，这种系统的评估方法可以显著支持知识发现应用中的基因组表达分析。(C)2002 Elsevier Science B.V.保留所有权利。
Several clustering algorithms have been suggested to analyse genome expression data, but fewer solutions have been implemented to guide the design of clustering-based experiments and assess the quality of their outcomes. A cluster validity framework provides insights into the problem of predicting the correct the number of clusters. This paper presents several validation techniques for gene expression data analysis. Normalisation and validity aggregation strategies are proposed to improve the prediction about the number of relevant clusters. The results obtained indicate that this systematic evaluation approach may significantly support genome expression analyses for knowledge discovery applications. (C) 2002 Elsevier Science B.V. All rights reserved.