Automated calibration of consensus weighted distance-based clustering approaches using sharp.

ABSTRACT:

Motivation

In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms.

Results

We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximizing the sharp score

SUBMITTER: Bodinier B 

PROVIDER: S-EPMC10627366 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature