2 votes
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What happens if all the features are correlated with each other before clustering?

Essentially, yes. One feature predicts to a reasonably high degree what the other features look like, so the additional features have limited discriminatory power. Obviously there is some effect, as ...
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2 votes
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How to tackle the human error made in labeling datasets for classification tasks like facial expression recognition?

In general the only way to deal with this is by quantifying these labeling mistakes in the output of the model, since the model will learn for them. And in many cases these are not really mistakes, ...
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1 vote
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What clustering algorithms work best for datasets with only binary categorical features?

Any clustering algorithm should work -- the main issue is the similarity or distance metric that determines how similar (or different) two elements are. This is often something like Euclidean distance,...
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1 vote

How to handle list features in clustering?

You essentially want to have a numerical value to represent the similarity of the lists of two distinct objects. There are a number of metrics to deal with that, eg the Jaccard Index or Dice's ...
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