K-means clustering algorithm based on improved artificial bee colony algorithm

doi:10.11772/j.issn.1001-9081.2014.04.1065

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (4): 1065-1069.DOI: 10.11772/j.issn.1001-9081.2014.04.1065

• Artificial intelligence • Previous Articles Next Articles

K-means clustering algorithm based on improved artificial bee colony algorithm

YU Jinping,ZHENG Jie,MEI Hongbiao

School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China

Received:2013-10-24 Revised:2013-12-11 Online:2014-04-29 Published:2014-04-01
Contact: ZHENG Jie

基于改进人工蜂群算法的K均值聚类算法

喻金平,郑杰,梅宏标

江西理工大学信息工程学院，江西赣州 341000

通讯作者: 郑杰
作者简介:喻金平(1964-),男,江西南昌人,教授,主要研究方向:数据挖掘;
郑杰(1990-),男,安徽六安人,硕士,主要研究方向:数据挖掘、群体智能;
梅宏标(1976-),男,江西南昌人,副教授,博士,主要研究方向:大规模仿真系统工程。
基金资助:
江西省教育厅自然科学基金项目;江西省研究生创新专项基金项目

Abstract

Abstract:

In order to overcome the disadvantages of the K-Means Clustering (KMC) algorithm, such as the poor global search ability, being sensitive to initial cluster centroid, as well as the initial random, being vulnerable to trap in local optima and the slow convergence velocity in later period of the original Artificial Bee Colony (ABC) algorithm, an Improved ABC (IABC) algorithm was proposed. IABC algorithm adopted the max-min distance product algorithm for initial bee colony to form a fitness function, which is adapted to the KMC algorithm, and a position updating method based on the global leading to enhance the efficiency of the iterative optimization process. The combination of the IABC and KMC (IABC-Kmeans) would improve the efficiency of clustering. The simulation experiments were conducted on the four standard test functions including Sphere, Rastrigin, Rosenbrock and Griewank and the UCI standard data sets. The experimental results indicate that IABC algorithm has a fast convergence speed, and overcomes the defect of the original algorithm being easily falling into local optimal solution; IABC-Kmeans has better clustering quality and general performance.

摘要：

针对K均值聚类(KMC)算法全局搜索能力差、初始聚类中心选择敏感,以及原始人工蜂群(ABC)算法的初始化随机性、易早熟、后期收敛速度慢等问题，提出了一种改进人工蜂群算法(IABC)。该算法利用最大最小距离积方法初始化蜂群,构造出适应KMC算法的适应度函数以及一种基于全局引导的位置更新公式以提高迭代寻优过程的效率。将改进的人工蜂群算法与KMC算法结合提出IABC-Kmeans算法以改善聚类性能。通过Sphere、Rastrigin、Rosenbrock和Griewank四个标准测试函数和UCI标准数据集上进行测试的仿真实验表明，IABC算法收敛速度快，克服了原始算法易陷入局部最优解的缺点；IABC-Kmeans算法则具有更好的聚类质量和综合性能。

CLC Number:

TP18
TP301.6

YU Jinping ZHENG Jie MEI Hongbiao. K-means clustering algorithm based on improved artificial bee colony algorithm[J]. Journal of Computer Applications, 2014, 34(4): 1065-1069.

喻金平郑杰梅宏标. 基于改进人工蜂群算法的K均值聚类算法[J]. 计算机应用, 2014, 34(4): 1065-1069.

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K-means clustering algorithm based on improved artificial bee colony algorithm

基于改进人工蜂群算法的K均值聚类算法

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