Spectral clustering – Towards Data Science

Clustering is a widely used unsupervised learning method. The grouping is such that points in a cluster are similar to each other, and less similar to points in other clusters. Thus, it is up to the algorithm to find patterns in the data and group it for us and, depending on the algorithm used, we may end up with different clusters. There are 2 broad approaches for clustering: 1. Compactness -- Points that lie close to each other fall in the same cluster and are compact around the cluster center. The closeness can be measured by the distance between the observations.