In this chapter, we're going to introduce the basic concepts of clustering and the structure of some quite common algorithms that can solve many problems efficiently. However, their assumptions are sometimes too restrictive; in particular, those concerning the convexity of the clusters can lead to some limitations in their adoption. After reading this chapter, the reader should be aware of the contexts where each strategy can yield accurate results and how to measure the performances and make the right choice regarding the number of clusters.
In particular, we are going to discuss the following:
- The general concept of clustering
- The k-Nearest Neighbors (k-NN) algorithm
- Gaussian mixture
- The K-means algorithm
- Common methods for selecting the optimal number of clusters (inertia, silhouette plots, Calinski-Harabasz index, and cluster instability)
- Evaluation...
