期刊名称：Bonfring International Journal of Data Mining
印刷版ISSN：2250-107X
电子版ISSN：2277-5048
出版年度：2014
卷号：4
期号：4
页码：26-33
DOI：10.9756/BIJDM.6140
语种：English
出版社：Bonfring
摘要：Cluster analysis in microarray gene expression studies is used to find groups of correlated and co-regulated genes. Several clustering algorithms are available in the literature. However no single algorithm is optimal for data generated under different technological platforms and experimental conditions. It is possible to combine several clustering methods and solutions using an ensemble approach. The method also known as consensus clustering is used here to examine the robustness of cluster solutions from several different algorithms. The method proposed here also is useful for estimating the number of clusters in a dataset. Here we examine the properties of consensus clustering using real and simulated datasets
