Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of

Background: Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. Results: We consider five such measures: Clest, Consensus (Consensus Clustering), FOM (Figure of Merit), Gap (Gap Statistics) and ME (Model Explorer), in addition to the classic WCSS (Within Cluster Sum-of-Squares) and KL (Krzanowski and Lai index). We perform extensive experiments on six benchmark microarray datasets, using both Hierarchical and K-means clustering algorithms, and we provide an analysis assessing both the intrinsic ability of a measure to predict the correct number of clusters in a d...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Raffaele Giancarlo, Davide Scaturro, Filippo Utro
• Keywords: BMCBI 2008, Internal Measures, Internal Validation Measures, Microarray Datasets