Machine Learning Algorithms

Clustering Fundamentals

In this chapter, we're going to introduce the basic concepts of clustering and the structure of k-means, a quite common algorithm that can solve many problems efficiently. However, its assumptions are very strong, in particular those concerning the convexity of the clusters, and this can lead to some limitations in its adoption. We're going to discuss its mathematical foundation and how it can be optimized. Moreover, we're going to analyze two alternatives that can be employed when k-means fails to cluster a dataset. These alternatives are DBSCAN, (which works by considering the differences of sample density), and spectral clustering, a very powerful approach based on the affinity among points.