Clustering with flexible constraints and application to disease subtyping
Clustering algorithms are widely used to extract knowledge from large amount of unlabeled data (such as, discovering subtypes of complex diseases to enable personalized treatments of patients). Clustering is a challenging problem because given the same data, samples can be grouped in multiple differ...
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| Online Access: | http://hdl.handle.net/2047/D20262510 |