Among the following clustering techniques, which ones can reject outliers? (in their classical formulation)

What is the K-Means algorithm trying to optimize?

Link each hierarchical clustering linkage technique (distance between two clusters) to the correct illustration.

Usually, a given sample can be assigned to a single cluster only, but there are clustering variants which can assign a weighted assignment of samples to multiple clusters.

How are such variants called?

Among the following applications, which one can be implemented with clustering?

When training a clustering model, what kind of inputs are we expecting?

When training a clustering model, what kind of target function (f : x ↦ y) are we learning?

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