Yeung Ka Yee, Medvedovic Mario, Bumgarner Roger E
Department of Microbiology, University of Washington, Seattle, WA 98195, USA.
Genome Biol. 2003;4(5):R34. doi: 10.1186/gb-2003-4-5-r34. Epub 2003 Apr 25.
Clustering is a common methodology for the analysis of array data, and many research laboratories are generating array data with repeated measurements. We evaluated several clustering algorithms that incorporate repeated measurements, and show that algorithms that take advantage of repeated measurements yield more accurate and more stable clusters. In particular, we show that the infinite mixture model-based approach with a built-in error model produces superior results.
聚类是分析阵列数据的常用方法，许多研究实验室正在生成带有重复测量的阵列数据。我们评估了几种纳入重复测量的聚类算法，并表明利用重复测量的算法能产生更准确、更稳定的聚类。特别是，我们表明基于无限混合模型且带有内置误差模型的方法能产生更优结果。
