The PSC (Particle Swarm Clustering) algorithm is an adaptation of the PSO (Particle Swarm Optimization) algorithm, and, therefore, follows a heuristic inspired by the optimization version. The particles move in the search space in order to become representatives of the natural groups of the database. The movement of particles is based on the behavior of social animals, like a flock of birds or a school of fish, which adjust their movements to defend the group and retrieve food. However, this metaphor for the PSC algorithm does not converge naturally, the algorithm must use an artificial parameter, called inertia term (ω), to ensure convergence. This paper proposes a simple modification to the PSC algorithm, resulting in the Modified Particle Swarm Clustering (mPSC) algorithm, by modifying the metaphor of human social order to eliminate the artificial parameter of the system and, consequently, the memory of the particles' velocity.