Python Data Analysis

Clustering with affinity propagation

Clustering aims to partition data into groups called clusters. Clustering is usually unsupervised in the sense that no examples are given. Some clustering algorithms require a guess for the number of clusters, while other algorithms don't. Affinity propagation falls in the latter category. Each item in a dataset can be mapped into Euclidean space using feature values. Affinity propagation depends on a matrix containing Euclidean distances between data points. Since the matrix can quickly become quite large, we should be careful not to take up too much memory. The scikit-learn library has utilities to generate structured data. Create three data blobs, as follows:

x, _ = datasets.make_blobs(n_samples=100, centers=3, ...