Quantum Particles Model for Data Clustering in Enterprise Computing

This paper presents a new generalized quantum particle model for data self-organizing clustering. The stochastic motion and collision of quantum particles give rise to a stochastic process of quantum entanglement of particles. The stationary probability distribution over the configuration space of entangled particles results in the optimally clustering solution of the given data set. The quantum particle model has advantages in terms of the insensitivity to noise, the quality robustness to clustered data, the learning ability, and the suitability for high-dimensional multi-shape large-scale data sets. In comparison with the classical version of particle model and the cellular automata, the quantum particle mode has much faster speed and higher quality for clustering. The simulation and comparison show the effectiveness and good performance of the proposed quantum particle approach to data clustering.