基于遗传算法的一种改进的K-均值聚类算法  

ZHANG Chunkai, WANG Lijun. Improved K-means clustering algorithm based on genetic algorithm.

作　　者：张春凯[1] 王丽君[2] 

机构地区：[1]江苏食品职业技术学院计算机应用技术系,江苏淮安223003 [2]河北北方学院图书馆,河北张家口075000

出　　处：《计算机工程与应用》2012年第26期144-147,共4页Computer Engineering and Applications

摘　　要：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。The traditional K-mean algorithm has the shortcoming that plunges into a local optimum prematurely be- cause of sensitive selection of the initial cluster center, this paper combines the genetic algorithm and K-means algo- rithm 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 validi- ty 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 con- vergence rate.

关 键 词：遗传算法 数据挖掘 K-均值 聚类 DB-Index 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]