Clustering: how much bias do we need?

Author:

Lorimer Tom1,Held Jenny2,Stoop Ruedi1ORCID

Affiliation:

1. Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

2. Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland

Abstract

Scientific investigations in medicine and beyond increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

Funder

Swiss National Science Foundation

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

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