ClusteringAlgorithms

Clustering in machine learning has a variety of applications, but how do you know which algorithm is best suited to your data? Here’s how to amplify your data insights with comparison metrics, including the F-measure.

Clustering algorithms are very important to unsupervised learning and are key elements of machine learning in general. These algorithms give meaning to data that are not labelled and help find structure in chaos. But not all clustering algorithms are created equal; each has its own pros and cons.

In this article, Toptal Freelance Software Engineer Lovro Iliassich explores a heap of clustering algorithms, from the well known K-Means algorithm to the elegant, state-of-the-art Affinity Propagation technique.