A novel multi-scale parallel artificial immune clone algorithm for unsupervised clustering(MSPAICC) is
presented, in which, evolutions of subgroups are performed in parallel with the different mutation strategies. The mutation
capability of an individual is determined by the competition among subgroups and subgroup fitness value. The larger mutation
operator is used to quickly localize the global optimal space at the early evolution, while the smaller mutation operator whose
scale gradually reduces are adopted to improve the local search ability at the later evolution. The experimental results show
the proposed method can improve clustering performance and the robustness compared with other clustering algorithms.