Draft:Minimax linkage
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In computational mathematics/statistics, minimax linkage is a criterion applied in hierarchical cluster analysis. Minimax linkage hierarchical clustering is a special case of the hierarchical clustering approaches, originally first introduced by Ao et al.[1] in the AI genomics software project CLUSTAG in 2004. Medical institutions have been deploying the minimax linkage hierarchical clustering in their genomics research. Jacob Bien and Robert Tibshirani (2011)[2] investigated the theoretical properties of the minimax linkage hierarchical clustering. Xiao Hui Tai and Kayla Frisoli (2021)[3] conducted benchmarking for the minimax linkage hierarchical clustering. The development history of the minimax linkage criterion is shown as follows.
Minimax linkage in genomics applications[edit]
The complete linkage hierarchical clustering, minimax linkage hierarchical clustering and set cover algorithms were implemented in the program CLUSTAG for tag SNP selection.
Theoretical properties of minimax linkage[edit]
Benchmarking the minimax linkage hierarchical clustering[edit]
Bien and Tibshirani (2011)[2] used two real datasets to demonstrate the appeal of using minimax linkage compared with other linkages.
Tai and Frisoli (2021)[3] reported that, similarly to Bien and Tibshirani (2011), minimax linkage often produced the smallest distances to prototypes, meaning that objects in a cluster were tightly clustered around their prototype.
References[edit]
- ^ Ao, Sio Iong; Yip, K.; Ng, M.; Cheung, D.; Fong, P.-Y.; Melhado, I.; Sham, P. C. (advance online: 2004-12-07). "CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs". Bioinformatics. 21 (8): 1735–1736.
- ^ a b Bien, Jacob; Tibshirani, Robert (2011). "Hierarchical Clustering With Prototypes via Minimax Linkage". Journal of the American Statistical Association. 106 (495): 1075–1084. doi:10.1198/jasa.2011.tm10183. PMC 4527350. PMID 26257451.
- ^ a b Tai, Xiao Hui; Frisoli, Kayla. "Benchmarking Minimax Linkage in Hierarchical Clustering". In: Data Analysis and Rationality in a Complex World, Springer, 2021.