I have an ML problem where I have a large data set and in this data set there are N categories. We have no labels. I want to be able to take this data set and use it to train a neural network to classify new instances. To this end, I believe I have to run an unsupervised learning algorithm like K-means and get the categories. This forms a set, Cat which I want to inspect by hand and give all the nice labels for so they are human readable. Then I want use this set of classified examples as a set of labelled data. I will then train a neural network to classify according to this set of labelled data. Does this make sense? Is there a single algorithm that can do this?
Someone has pointed out that, without labels, only an unsupervised algorithm is useful. What I am wondering is the following, and it is a dumb question: After I have ran k-means and classified N samples, can I use this to quickly determine the class that a new sample is in?