Statistical Significance for Hierarchical Clustering

• DOI: 10.48550/arxiv.1411.5259
• Publication Date: 2014-01-01
• Authors: Patrick K. Kimes; Yufeng Liu; D. Neil Hayes; J. S. Marron
• Keywords: Hierarchical clustering; Brown clustering; Consensus clustering; Single-linkage clustering

Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised of high dimensional datasets. Among methods clustering, hierarchical approaches have enjoyed substantial popularity in genomics other fields their ability simultaneously uncover multiple layers clustering structure. A critical challenging question cluster is whether identified clusters represent important underlying structure or are artifacts natural sampling variation. Few been proposed addressing this problem context which further complicated by tree partition, multiplicity tests required parse nested clusters. In paper, we propose a Monte Carlo based approach testing statistical significance addresses these issues. The implemented as sequential procedure guaranteeing control family-wise error rate. Theoretical justification provided our approach, its power detect true illustrated through several simulation studies applications two cancer gene expression