Removing confounding factors via constraint-based clustering

Jingjing Liu
Jingjing Liu
Brian C. Healy
Brian C. Healy
Tanuja Chitnis
Tanuja Chitnis

Artificial Intelligence in Medicine, Volume 65, Issue 2, 2015, Pages 79-88.

Cited by: 1|Bibtex|Views7|DOI:https://doi.org/10.1016/j.artmed.2015.06.004
EI
Other Links: dl.acm.org|academic.microsoft.com|www.sciencedirect.com

Abstract:

ObjectivesConfounding 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 ...More

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