Removing confounding factors via constraint-based clustering
Artificial Intelligence in Medicine, Volume 65, Issue 2, 2015, Pages 79-88.
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
Full Text (Upload PDF)
PPT (Upload PPT)