[1]凌 静,江凌云,赵 迎.结合模拟退火算法的遗传 K-Means 聚类方法[J].计算机技术与发展,2019,29(09):61-65.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 012]
　LING Jing,JIANG Ling-yun,ZHAO Ying.A Genetic K-Means Clustering Method Combined with Simulated Annealing Algorithm[J].,2019,29(09):61-65.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 012]
点击复制

结合模拟退火算法的遗传 K-Means 聚类方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年09期
页码:
61-65
栏目:
智能、算法、系统工程
出版日期:
2019-09-10

文章信息/Info

Title:
A Genetic K-Means Clustering Method Combined with Simulated Annealing Algorithm
文章编号:
1673-629X(2019)09-0061-05
作者:
凌 静; 江凌云; 赵 迎
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
LING Jing; JIANG Ling-yun; ZHAO Ying
School of Telecommunications &Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
聚类; K-Means 算法; 遗传算法; 模拟退火算法
Keywords:
clustering; K-Means algorithm; genetic algorithm; simulated annealing algorithm
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 09. 012
摘要:
K-Means 算法是一种经典的基于划分的聚类方法。传统的 K-Means 算法中存在很明显的缺陷,它对初始聚类中心的依赖性很大,聚类结果很容易陷入局部最优值;而基于遗传算法改进的 K-Means 聚类方法,提高了聚类结果的稳定性,但因为个体的多样性不足,常常会出现早熟等现象,其局部寻优能力较弱。 针对上述问题,文中提出一种结合模拟退火算法的遗传 K-Means 聚类方法。 利用模拟退火算法改进遗传算法的变异操作,用 K-Means 操作取代遗传算法的交叉操作,改善早熟现象,避免聚类结果陷入局部最优,实现聚类方法性能的提升。 实验结果表明,该方法的聚类准确度比一般 K-Means 方法和遗传 K-Means 方法都要高。
Abstract:
K-Means algorithm is one of the most classical division-based clustering methods. In the traditional K-Means algorithm,there are obvious flaws like strong dependence on the initial clustering center and the clustering result is easy to fall into the local optimal value. The improved K-Means clustering method based on genetic algorithm improves the stability of clustering results. However,due to the insufficient diversity of individuals,prematurity and other phenomena often occur,and its local optimization is weak. For this,we present a genetic K-Means clustering method combined with simulated annealing algorithm. The simulated annealing algorithm is used to improve the mutation operation of genetic algorithm,the classical K-Means operation is used to replace the crossover operation of the genetic algorithm,so as to improve the premature phenomenon,avoid the clustering result falling into the local optimal,and improve the performance of the clustering method. The experiment shows that the clustering accuracy of the proposed method is higher than that of the general K-Means method and the genetic K-Means method.

相似文献/References:

[1]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
　JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(09):36.
[2]张甜 罗眉 孟晓红 赵宗涛.一种基于状态特征的航天发射故障诊断技术[J].计算机技术与发展,2010,(01):93.
　ZHANG Tian,LUO Mei,MENG Xiao-hong,et al.A Technology in Fault Diagnosis of Spaceflight Launch Based on State Character[J].,2010,(09):93.
[3]王会颖 章义刚.求解聚类问题的改进人工鱼群算法[J].计算机技术与发展,2010,(03):84.
　WANG Hui-ying,ZHANG Yi-gang.An Improved Artificial Fish- Swarm Algorithm of Solving Clustering Analysis Problem[J].,2010,(09):84.
[4]赵敏 倪志伟 刘斌.K—means与朴素贝叶斯在商务智能中的应用[J].计算机技术与发展,2010,(04):179.
　ZHAO Min,NI Zhi-wei,LIU Bin.Application Research of K - Means Clustering and Naive Bayesian Algorithm in Business Intelligence[J].,2010,(09):179.
[5]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
　WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].,2010,(09):109.
[6]耿波 仲红 徐杰 闫娜娜.用关联分析法对负荷预测结果进行二次处理[J].计算机技术与发展,2008,(04):171.
　GENG Bo,ZHONG Hong,XU Jie,et al.Using Correlation Analysis to Treat Load Forecasting Results[J].,2008,(09):171.
[7]游芳 姜建国 张坤.基于二维属性的高维数据聚类算法研究[J].计算机技术与发展,2009,(05):111.
　YOU Fang,JIANG Jian-guo,ZHANG Kun.Cluster- Algorithm Studies Based on Two- Dimensional Attribute Higher - Dimension Data[J].,2009,(09):111.
[8]刘淑英 程国建 彭方.人工神经生长细胞结构网络在医疗诊断的应用[J].计算机技术与发展,2009,(05):231.
　LIU Shu-ying,CHENG Guo-jian,PENG Fang.Applications of Growing Cell Structures of Artificial Neural Network for Medical Diagnosis[J].,2009,(09):231.
[9]范新 沈闻 丁泉勋 沈洁.基于正例和未标文档的半监督分类研究[J].计算机技术与发展,2009,(06):58.
　FAN Xin,SHEN Wen,DING Quan-xun,et al.Research on Semi- Supervised Classification Based on Positive and Unlabeled Text Document[J].,2009,(09):58.
[10]王园园 倪志伟 赵裕啸 伍章俊.基于决策树的模糊聚类评价算法及其应用[J].计算机技术与发展,2009,(09):232.
　WANG Yuan-yuan,NI Zhi-wei,ZHAO Yu-xiao,et al.Fuzzy Clustering Evaluation Algorithm Based on Decision Tree and Application[J].,2009,(09):232.

更新日期/Last Update: 2019-09-10