A New Data-Mining Approach: Self-Organizing Entanglement Dynamics of Quantum Particles
Dianxun Shuai, Qing Yan Shuai · 2008
Most of currently used approaches to data mining are not qualified to quickly cluster a high-dimensional large-scale database. This paper is devoted to a novel data-mining model based on self-organizing entanglement dynamics of generalized quantum particles (GQP). The GQP approach transforms the data mining process into astochastic dynamical process of particle motion, collision and quantum entanglement of generalized quantum particles on a particle array. In comparison with the GPM (Generalized Particle Model) method we have proposed before, the GQP data-mining approach has much fasterspeed and higher quality. The GQP-based approach also 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 show the effectiveness and good performance of the proposed GQP approach to data mining.