I am beginner to data mining.
This is my understanding, regression is used to predict continuous values. It is a type of classification.
Classification is Supervised and Clustering is unsupervised.
In classification we have predefined classes, whereas in clustering there are no classes defined.
What does this exactly mean? But in both of them we do have training data, which tells which output the set of features should belong to. Then how can we say that clustering does not have per-defined classes?
Please explain when to use classification and clustering.
Can clustering also predict continuous values?
Thanks. Please help.
This post has been edited.
Best Answer
As @chl says, there are good threads on this site regarding supervised versus unsupervised learning. In regards to your bolded question: you're misunderstanding what data is supplied to supervised versus unsupervised methods. Supervised methods will always include an additional piece of information for each sample: the correct answer.