Question Detail: Right now I am trying to understand Clustering-methods. What I understood by now:
- In supervised learning the categories, data is assigned to are known before computation. So they 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?
Asked By : Prot
Best Answer from StackOverflow
Question Source : http://cs.stackexchange.com/questions/2907
Answered By : Dave Clarke
The difference is that in supervised learning the ‘categories’ 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.
You could use something like principle component analysis to help determine the relevant ones.
