An Overview of the scikit-learn Clustering Package

Clustering is an unsupervised Machine Learning technique, where there is neither a training set nor predefined classes. Clustering is used when there are many records, which should be grouped according to similarity criteria, such as distance. A clustering algorithm takes a dataset as input and returns a list of labels as output, corresponding to the associated clusters. Cluster analysis is an iterative process where, at each step, the current iteration is evaluated and used to feedback into changes to the algorithm in the next iteration, until the desired result is obtained. The scikit-learn library provides a subpackage, called sklearn.cluster, which provides the most common clustering algorithms.