摘要
针对传统的k-means算法执行的结果过度依赖与初始聚类中心的选择,容易陷入局部最优解,全局搜索能力不强等缺点,该文提出将遗传算法(GA)与模拟退火算法(SA)相结合的混合遗传算法,此方法比传统的遗传算法具有更好的全局收敛性,避免了早熟的问题。将此算法应用到k-means聚类领域中,可以有效的克服k-means算法的缺陷,同时优化聚类的结果。实验表明该方法是一种高效精确的聚类方法。
As K-means Clustering Algorithm is excessively depend on the choice of the initial cluster centers, it leads to be involved in lo- cally optimal solution and does not have comprehensive ability to search. We propose a method based on Genetic algorithms and Simulated annealing called Genetic Simulated annealing algorithm. The new method has better Global convergence compared with Genetic algo- rithms. It also overcomes the aspect of premature convergence problem. So we can apply this algorithms to the K-means Clustering Meth- od so as to overcome defect of K-means Clustering Method and optimize the result of clustering . Experiments indicated that this algo- rithm is efficient and accurate.
作者
张济强
高玉良
ZHANG Ji-qiang, GAO Yu-liang (DalianJiaotong University,Software college, Dalian 116028, China)
出处
《电脑知识与技术》
2012年第3期1611-1613,1617,共4页
Computer Knowledge and Technology