基于遗传算法的一种改进的K-均值聚类算法

传统K-均值算法对初始聚类中心敏感大,易陷入局部最优值.将遗传算法与K均值算法结合起来进行探讨并提出一种改进的基于K-均值聚类算法的遗传算法,改进后的算法是基于可变长度的聚类中心的实际数目来实现的.同时分别设计出新的交叉算子和变异算子,并且使用的聚类有效性指标DB-Index作为目标函数,该算法很好地解决了聚类中心优化问题,与之前的两种算法相比,改进后的算法改善了聚类的质量,提高了全局的收敛速度.

Improved K-means clustering algorithm based on genetic algorithm

The traditional K-mean algorithm has the shortcoming that plunges into a local optimum prematurely because of sensitive selection of the initial cluster center,this paper combines the genetic algorithm and K-means algorithm and presents a genetic algorithm based on K-means clustering algorithm,the algorithm is realized using actual real number of variable length cluster center.It designs new crossover and mutation operators and uses cluster validity index DB-Index as the target function,the problem of optimizing cluster center is solved by algorithm.Compared with the previous two algorithms,this algorithm improves the clustering quality effectively,improves the global convergence rate.