I am trying to understand clustering methods.
What I I think I understood:
In supervised learning, the categories/labels data is assigned to are known before computation. So, the labels, classes or categories are being used in order to "learn" the parameters that are really significant for those clusters.
In unsupervised learning, datasets are assigned to segments, without the clusters being known.
Does that mean that, if I don't even know which parameters are crucial for a segmentation, I should prefer supervised learning?