ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2017
Clustering: how much bias do we need?
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- What characteristics are most important in stratifying patients into groups with different risk of diabetic foot ulceration?Journal of diabetes investigation · 2024Article
- Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.International journal of molecular sciences · 2022Article
- Mathematical methods in medicine: neuroscience, cardiology and pathology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2017Review
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Authors and funding
3 authors.
Funding
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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'.
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Registered trials
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