In class, we covered the simple Bayesian classifier. This form of classification falls under a category known as supervised learning. What this means is that a set of labelled data data is used to to "train" the underlying model. However, it is not always possible to have such a data set, yet we may still wish to discover some form of underlying structure in an unlabelled data set. Such a task falls under the category of unsupervised learning.

Clustering is a form of unsupervised learning. "Clustering is the problem of identifying groups, or clusters of data points in multidimensional space". For example, consider the following data set:

In class we covered various forms of supervised learning, such as classification and density estimation. These applications required the use of training data. Supervised learning, as i.e. learning using training data. Clustering, on the other hand is a form of unsupervised learning. We are not given a training set, yet wish to discover some form of underling structure in a dataset.

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