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Cluster analysis methods are used to identify homogeneous subgroups in a dataset. In biomedical applications, one frequently applies cluster analysis inorder to identify biologically interesting subgroups. In particular, one maywish to identify subgroups that are associated with a particular outcome ofinterest. Conventional clustering methods generally do not identify suchsubgroups, particularly when there are a large number of high-variance featuresin the data set. Conventional methods may identify clusters associated withthese high-variance features when one wishes to obtain secondary clusters thatare more interesting biologically or more strongly associated with a particularoutcome of interest. A modification of sparse clustering can be used toidentify such secondary clusters or clusters associated with an outcome ofinterest. This method correctly identifies such clusters of interest in severalsimulation scenarios. The method is also applied to a large prospective cohortstudy of temporomandibular disorders and a leukemia microarray data set.

Identification of relevant subtypes via preweighted sparse clustering