Kernel methods for point symmetry-based clustering
Generalization (by kernelization) of a family of point symmetry distances. Kernelized-SBKM that offers new possibilities for point symmetry-based clustering. Empirical recognition of symmetric clusters using any proximity measure. Highlighting new simple examples, hard to manage by original methods. New complex examples well-managed with KSBKM by using implicit projections.
clustering pattern recognition point symmetry-based distance measure kernel function k-means