Fundamentals to clustering high-dimensional data (3D point clouds)

Clustering algorithms allow data to be partitioned into subgroups, or clusters, in an unsupervised manner. Intuitively, these segments group similar observations together. Clustering algorithms are therefore highly dependent on how one defines this notion of similarity, which is often specific to the field of application. Why unsupervised segmentation & clustering is the “bulk of AI”? What to look for when using them? How to evaluate performances? Explications and Illustration over 3D point cloud data.