Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients
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 from the data as other features will be correlated with age. Motivated by the need to find homogeneous...
Paper Details
Title
Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients
Published Date
Oct 1, 2015
Volume
65
Issue
2
Pages
79 - 88
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