I am currently reading about the guassian mixture model and the expectation–maximization algorithm. From what I am reading the two differences between the two here is what I've come up with so far, are they correct?
- GMM is a model, it's a way to represent the data (subpopulations of the entire population)
- EM is an algorithm, it is a process to represent a specific model (GMM in this case)
My final question is, let's say you have a population, I've been reading that you must define k (amount of clusters) before using an algorithm (EM in this case). Is this related to supervised vs. unsupervised algorithms? In other words, in supervised algorithms you define how many clusters there are in advance and train the cluster, while unsupervised would calculate the amount of clusters it's own. Is this the reason why you must define k?