Two-way combinatorial clustering network
Shengyu Cao, Laifu Liu · 2010
Two trends in clustering (also called unsupervised classification) problem: from one-way to two-way and from tree structure to net structure, are integrated in this paper to a framework of two-way combinatorial clustering network (TWCCN). The theory of directed branch-connected tree (DBCT) is constructed to describe the model of TWCCN, and algorithms based on nonnegative matrix factorization (NMF) called bootstrap NMF are proposed to build TWCCN. We show the method make sense take examples for the clustering of gene expression data and the problem of phylogenetics in bioinformatics.