A Dirichlet model of alignment cost in mixed-membership unsupervised clustering.

ABSTRACT: Mixed-membership unsupervised clustering is widely used to extract informative patterns from data in many application areas. For a shared data set, the stochasticity and unsupervised nature of clustering algorithms can cause difficulties in comparing clustering results produced by different algorithms, or even multiple runs of the same algorithm, as outcomes can differ owing to permutation of the cluster labels or genuine differences in clustering results. Here, with a focus on inference of individual genetic ancestry in population-genetic studies, we study the cost of misalignment of mixed-membership unsupervised clustering replicates under a theoretical model of cluster memberships. Using Dirichlet distributions to model membership coefficient vectors, we provide theoretical results quan

SUBMITTER: Liu X 

PROVIDER: S-EPMC10656040 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature