In this work, we investigate swarm self-clustering, where robots autonomously organize into spatially coherent groups using only local sensing and decision-making, without external commands, global positioning, or inter-robot communication. Each robot forms and maintains clusters by responding to relative distances from nearby neighbors detected through onboard range sensors with limited fields of view. The method is suited for GPS-denied and communication-constrained environments and requires no prior knowledge of cluster size, number, or membership. A mechanism enables robots to alternate between consensus-based and random goal assignment based on local neighborhood size, ensuring robustness, scalability, and untraceable clustering independent of initial conditions. Extensive simulations and real-robot experiments demonstrate empirical convergence, adaptability to dynamic additions, and improved performance over local-only baselines across standard cluster quality metrics.