CAJ | 학술논문

提出了一种应用广义量子粒子模型进行自组织聚类的新方法.该模型将数据聚类过程转化为一个量子粒子在状态构形空间上的随机自组织过程,由量子粒子之间相互纠缠形成的状态构形随时间不断演化,最终会收敛到一个平稳的概率分布,最优状态空间构形与平稳概率分布中具有最大概率的状态构形相对应.对此自组织过程的收敛性进行了理论上的证明.与传统的适用于大规模数据的聚类方法相比较,该算法具有更快的收敛速度,仿真实验表明了其优越性.
제출료일충응용엄의양자입자모형진행자조직취류적신방법.해모형장수거취류과정전화위일개양자입자재상태구형공간상적수궤자조직과정,유양자입자지간상호규전형성적상태구형수시간불단연화,최종회수렴도일개평은적개솔분포,최우상태공간구형여평은개솔분포중구유최대개솔적상태구형상대응.대차자조직과정적수렴성진행료이론상적증명.여전통적괄용우대규모수거적취류방법상비교,해산법구유경쾌적수렴속도,방진실험표명료기우월성.
A novel generalized quantum particle model(GQPM)was presented for data self-organizing clustering.In this model the data clustering process is transformed into a stochastic self-organizing process of the quantum particles in the state configuration space.The state configuration will evolve to a stationary probability distribution,and thus the optimal state configuration on particles can be obtained from the state configuration which has the highest probability in the stationary probability distribution.The convergence of the self-organizing process was proved in this paper.The GQPM algorithm has much faster clustering speed than the traditional clustering algorithm for the large scale database.Its superiorities were verified by the simulation experiments.