Removing Confounding Factors via Constraint Based Clustering: An Application to Finding Homogeneous Groups of Multiple Sclerosis Patients

Artificial Intelligence in Medicine, Volume 65, Issue 2, 2013, Pages 487-492.

Cited by: 11|Bibtex|Views12|DOI:https://doi.org/10.1109/ICHI.2013.75
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Other Links: dblp.uni-trier.de|dl.acm.org|academic.microsoft.com

Abstract:

Confounding factors in unsupervised data can lead to undesirable clustering results. For example in medical datasets, age is often a confounding factor in tests designed to judge the severity of a patient's disease through measures of mobility, eyesight and hearing. In such cases, removing age from each instance will not remove its effect...More

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