CAJ | 학술논문

该文针对K均值聚类算法存在的缺点,提出一种改进的粒子群优化(PSO)和K均值混合聚类算法.该算法在运行过程中通过引入小概率随机变异操作增强种群的多样性,提高了混合聚类算法全局搜索能力,并根据群体适应度方差来确定K均值算法操作时机,增强算法局部精确搜索能力的同时缩短了收敛时间.将此算法与K均值聚类算法、基于PSO聚类算法和基于传统的粒子群K均值聚类算法进行比较,数据实验证明,该算法有较好的全局收敛性,不仅能有效地克服其他算法易陷入局部极小值的缺点,而且全局收敛能力和收敛速度都有显著提高.
해문침대K균치취류산법존재적결점,제출일충개진적입자군우화(PSO)화K균치혼합취류산법.해산법재운행과정중통과인입소개솔수궤변이조작증강충군적다양성,제고료혼합취류산법전국수색능력,병근거군체괄응도방차래학정K균치산법조작시궤,증강산법국부정학수색능력적동시축단료수렴시간.장차산법여K균치취류산법、기우PSO취류산법화기우전통적입자군K균치취류산법진행비교,수거실험증명,해산법유교호적전국수렴성,불부능유효지극복기타산법역함입국부겁소치적결점,이차전국수렴능력화수렴속도도유현저제고.
To deal with the problem of premature convergence of the traditional K-means algorithm, a novel K-means cluster method based on the enhanced Particle Swarm Optimization(PSO) algorithm is presented. In this approach, the stochastic mutation operation is introduced into the PSO evolution, which reinforces the exploitation of global optimum of the PSO algorithm. In order to avoid the premature convergence and speed up the convergence, traditional K-means algorithm is used to explore the local search space more efficiently dynamically according to the variation of the particle swarm's fitness variance. Comparison of the performance of the proposed approach with the cluster method based on K-means, traditional PSO algorithm and other PSO-K-means algorithm is experimented. The experimental results show the proposed method can not only effectively solve the premature convergence problem, but also significantly speed up the convergence.