What exactly is the difference between supervised and unsupervised learning?

I am trying to understand clustering methods.

What I I think I understood:

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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.

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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?

The difference is that in supervised learning the "categories", "classes" or "labels" are known. In unsupervised learning, they are not, and the learning process attempts to find appropriate "categories". In both kinds of learning all parameters are considered to determine which are most appropriate to perform the classification.

Whether you chose supervised or unsupervised should be based on whether or not you know what the "categories" of your data are. If you know, use supervised learning. If you do not know, then use unsupervised.

As you have a large number of parameters and you do not know which ones are relevant, you could use something like principle component analysis to help determine the relevant ones.

StackExchange Computer Science Q#2907, answer score: 24