Machine Learning Algorithms - Second Edition

Clustering basics

Let's consider a dataset of m-dimensional samples:

Let's assume that it's possible to find a criterion (not a unique) so that each sample can be associated with a specific group according to its peculiar features and the overall structure of the dataset:

Conventionally, each group is called a cluster, and the process of finding the function, G, is called clustering. Right now, we are not imposing any restriction on the clusters; however, as our approach is unsupervised, there should be a similarity criterion to join some elements ...