Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
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DOI：
10.1093/bioinformatics/btm158
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发表时间：
2007-07-01
期刊：
影响因子：
5.8
通讯作者：
Datta, Somnath
中科院分区：
文献类型：
--
作者：
Pihur, Vasyl;Datta, Susmita;Datta, Somnath
动机：生物学家经常在微阵列数据分析的探索阶段采用聚类技术来发现相关的生物分组。鉴于机器学习文献中有许多聚类算法，用户可能希望选择一个对他/她的数据集或应用程序性能最好的算法。虽然多年来已经提出了各种验证措施来判断由给定的聚类算法产生的聚类的质量，包括它们的生物相关性，但不幸的是，给定的聚类算法在一个验证措施下表现不佳，而在另一个验证措施下表现优于许多其他算法。从多个验证措施的结果的手动合成在实践中几乎是不可能的，特别是，当大量的聚类算法是使用几个措施进行比较。需要一种自动和客观的方式来协调排名。结果如下：使用蒙特卡罗交叉熵算法，我们通过优化距离标准的加权聚合成功地联合收割机组合了一组正在考虑的聚类算法的排名。建议的加权等级聚合允许一个更客观和自动化的评估聚类结果比一个简单的视觉检查。我们说明了我们的程序使用一个模拟以及三个真实的基因表达数据集从不同的平台，我们排名共11个聚类算法，使用10个不同的验证措施的组合检查。对于给定数量的聚类k以及k的整个范围，发现聚合排名。
Motivation: Biologists often employ clustering techniques in the explorative phase of microarray data analysis to discover relevant biological groupings. Given the availability of numerous clustering algorithms in the machine-learning literature, an user might want to select one that performs the best for his/her data set or application. While various validation measures have been proposed over the years to judge the quality of clusters produced by a given clustering algorithm including their biological relevance, unfortunately, a given clustering algorithm can perform poorly under one validation measure while outperforming many other algorithms under another validation measure. A manual synthesis of results from multiple validation measures is nearly impossible in practice, especially, when a large number of clustering algorithms are to be compared using several measures. An automated and objective way of reconciling the rankings is needed. Results: Using a Monte Carlo cross-entropy algorithm, we successfully combine the ranks of a set of clustering algorithms under consideration via a weighted aggregation that optimizes a distance criterion. The proposed weighted rank aggregation allows for a far more objective and automated assessment of clustering results than a simple visual inspection. We illustrate our procedure using one simulated as well as three real gene expression data sets from various platforms where we rank a total of eleven clustering algorithms using a combined examination of 10 different validation measures. The aggregate rankings were found for a given number of clusters k and also for an entire range of k.