A novel quantum entanglement model for self-organizing data clustering

This paper presents a novel generalized quantum particle model (GQPM) for data self-organizing clustering (1). Using GQPM we transform the data Clustering into a stochastic process of partitioning equivalence classes of particles under the quantum entanglement relation. The GQPM approach has Much faster clustering speed and higher clustering quality than the nonquantum particle model GPM and GCA we proposed before. GQPM is also characterized by the self-organizing clustering and has advantages in terms of the insensitivity to noise, the quality robustness to clustered data, the learning ability, the suitability for high-dimensional multi-shape large-scale data sets. The Simulations and comparisons have shown the effectiveness and good performance of the proposed GQPM approach to data clustering.