Compared to a supervised classifier, the goal of clustering is to identify intrinsic groups in a set of unlabeled data. It can be applied to identifying representative examples of homogeneous groups, finding useful and suitable groupings, or finding unusual examples, such as outliers.
We'll demonstrate how to implement clustering by analyzing a bank dataset. The dataset consists of 11 attributes, describing 600 instances, with age, sex, region, income, marital status, children, car ownership status, saving activity, current activity, mortgage status, and PEP. In our analysis, we will try to identify the common groups of clients by applying the expectation maximization (EM) clustering.
EM works as follows: given a set of clusters, EM first assigns each instance with a probability distribution of belonging to a particular cluster. For example, if we start with three...
