摘要
模糊C-均值聚类算法通过迭代的爬山技术来寻找问题的最优解,是一种局部搜索算法,容易受初始值的影响而陷入局部极小值.遗传算法是一种应用广泛的全局优化算法,是一种与求解问题无关的算法模式,能够有效解决模糊C-均值聚类算法对初始化敏感的问题,利用改进后的遗传算法能更好地解决聚类问题.
The fuzzy C-means clustering algorithm climbing through iterative techniques to find the optimal solution is a local search algorithm,vulnerable to the effects of the initial value into a local minimum.Genetic algorithm is a widely used global optimization algorithms,and solving problems is a model-independent algorithm,can effectively solve the fuzzy C-means clustering algorithm to initialize the sensitive issue,the use of improved genetic algorithm to better clustering solution.
出处
《河南大学学报（自然科学版）》
CAS
北大核心
2012年第1期92-95,共4页
Journal of Henan University:Natural Science
关键词
模糊C-均值聚类
遗传算法
种群划分
fuzzy C-means clustering
genetic algorithm
population division
