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Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.

1
vote
You can turn this into a binary classification problem (worrisome behaviour vs non-worrisome behaviour). For such problems you can use a plethora of methods (logistic regression, SVM, neural networks, …
answered Jul 28 '14 by Marc Claesen
19
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This is called learning from positive and unlabeled data, or PU learning for short, and is an active niche of semi-supervised learning. Briefly, it is important to use the unlabeled data in the learn …
answered Sep 27 '15 by Marc Claesen