The k-means algorithm and its variants are popular techniques of clustering. Their purpose is to uncover group structures in a dataset. In actuarial applications, these methods detect clusters of policies with similar features and allows to draw a map of dominant risks. This working note starts with a review of the k-means algorithm and develops next two extensions to manage categorical features. We develop a mini-batch version that keeps computation time under control when analysing a high-dimensional dataset. We next introduce the fuzzy k-means in which policies can belong to multiple clusters. Finally, we conclude by a detailed introduction to spectral clustering.

Keywords: Clustering analysis, unsupervised learning, k-means, spectral clustering.