Selective inference for k-means clustering

Code

• github.com/yiqunchen/kmeansinference
  OfficialIn papernone★ 9
• github.com/yiqunchen/kmeansinference-experiments
  OfficialIn papernone★ 0

Abstract

We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we take a selective inference approach. We propose a finite-sample p-value that controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using k-means clustering, and show that it can be efficiently computed. We apply our proposal in simulation, and on hand-written digits data and single-cell RNA-sequencing data.

Tasks

Reproductions