Are clusterings of multiple data views independent?

ABSTRACT: In the Pioneer 100 (P100) Wellness Project, multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor personalized health recommendations to each subgroup. It is tempting to cluster the participants using all of the data types and timepoints, in order to fully exploit the available information. However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views. If this assumption does not hold, then clustering the participants using multiple data views may lead to spurious results. In this article,

SUBMITTER: Gao LL 

PROVIDER: S-EPMC8489535 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature