Self-Organizing Data Clustering Based on Quantum Entanglement Model
Dianxun Shuai, Yuzhe Liu, Qing Yan Shuai, Liangjun Huang, Yuming Dong · 2006
Most of currently used approaches to data clustering are not qualified to quickly cluster a high-dimensional large-scale database. This paper is devoted to a novel generalized quantum particle model (GQPM) to data self-organizing clustering. The GQPM approach transforms the data clustering process into a stochastic process of particle motion, collision and quantum entanglement on a particle array. In comparison with the GPM clustering method we have proposed before, the GQPM has much faster speed and higher quality for clustering. 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