[1]谷文祥,郭丽萍,殷明浩.模糊c-均值算法和万有引力算法求解模糊聚类问题[J].智能系统学报,2011,6(6):520-525.
GU Wenxiang,GUO Liping,YIN Minghao.A solution for a fuzzy clustering problem by applying fuzzy c-means algorithm and gravitational search algorithm[J].CAAI Transactions on Intelligent Systems,2011,6(6):520-525.
点击复制
GU Wenxiang,GUO Liping,YIN Minghao.A solution for a fuzzy clustering problem by applying fuzzy c-means algorithm and gravitational search algorithm[J].CAAI Transactions on Intelligent Systems,2011,6(6):520-525.
模糊c-均值算法和万有引力算法求解模糊聚类问题
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
6
期数:
2011年第6期
页码:
520-525
栏目:
学术论文—人工智能基础
出版日期:
2011-12-25
- Title:
- A solution for a fuzzy clustering problem by applying fuzzy c-means algorithm and gravitational search algorithm
- 文章编号:
- 1673-4785(2011)06-0520-06
- 关键词:
- 模糊聚类; 模糊c均值算法; 万有引力搜索算法; 模糊万有引力搜索算法
- Keywords:
- fuzzy clustering problem; fuzzy cmeans algorithm; gravitational search algorithm; fuzzy gravitational search algorithm
- 分类号:
- TP301.6
- 文献标志码:
- A
- 摘要:
- 针对单纯使用模糊c均值算法（FCM）求解模糊聚类问题的不足，首先，提出一种改进的万有引力搜索算法，通过一定概率按照不同方式对速度进行更新，有效增大了种群的搜索域.其次，提出了模糊万有引力搜索算法（FGSA）.最后，在模糊万有引力搜索算法（FGSA）和模糊c均值算法（FCM）的基础上，提出了一种新算法（FGSAFCM）来求解模糊聚类问题，有效避免了单纯使用模糊c均值算法时对初始值敏感且易于陷入局部最优的缺点.采用目标函数和有效性评价函数作为评价标准，选取10个经典数据集作为测试数据，实验结果表明，新算法比单一的模糊c均值算法有更高的准确性和鲁棒性.
- Abstract:
- Aiming at fixing the shortcomings of using fuzzy Cmeans algorithm solely to solve fuzzy clustering problems, first, this paper proposed an improved gravitational search algorithm by updating the velocity of individuals according to a probability, expanding the search space effectively. Secondly, a fuzzy gravitational search algorithm (FGSA) was proposed. Finally, a novel hybrid algorithm (FGSAFCM) based on a fuzzy improved gravitational search algorithm (FGSA) and fuzzy cmeans algorithm (FCM) was proposed to solve fuzzy clustering problems. Fuzzy cmeans algorithm is very sensitive to initialization and easily gets into local optima, but the new algorithm may avoid these shortcomings. This paper chooses the objective function and validity function as the evaluation criterion. The FGSAFCM was tested on ten classic datasets, and the experiment results show that the new algorithm is more accurate and robust than the sole fuzzy cmeans algorithm.
- 参考文献/References:
-
［1］DUBES R C, JAIN A K. Algorithms for clustering data［M］. Englewood Cliffs, USA: Prentice Hall, 1988: 120.
［2］BEZDEK J C. Pattern recognition with fuzzy objective function algorithms［M］. New York，USA: Plenum Press, 1981： 4393.
［3］KRISHNAPURAM R, KELL J M. The possibilistic cmeans algorithm: insights and recommendation［J］. IEEE Trans FS, 1996, 4(3): 385393.
［4］RASHEDI E, NEZAMABADIPOUR H, SARYAZDI S. GSA: a gravitational search algorithm［J］. Information Sciences, 2009, 179(13): 22322248.
［5］BEZDEK J C. Fuzzy mathematics in pattern classification［D］. Ithaca: USA Cornell University, 1973: 1842.
［6］DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact wellseparated clusters［J］. Journal of Cybernetics, 1973, 3(1): 3257.
［7］PANG W, WANG K, ZHOU C, et al. Fuzzy discrete particle swarm optimization for solving traveling salesman problem［C］//Proceedings of the Fourth International Conference on Computer and Information Technology. Wuhan，China: IEEE CS Press, 2004: 796800.
［8］PAL S K, DUTTA M R D. Fuzzy sets and decision making approaches in vowel and speaker recognition［J］. IEEE Transactions on Systems, Man, and Cybernetics，1977, 7(8): 625629.
［9］BEZDEK J C. Cluster validity with fuzzy sets［J］. Cybernetics, 1974, 3(3): 5873.
［10］RUNKLER T A, KATE C. Fuzzy clustering by particle swarm optimization［C］//International Conference on Fuzzy Systems Vancouver Canada, 2006：601608.
［11］WANG Yingje. Fuzzy clustering analysis by using genetic algorithm［J］. ICIC, 2008, 2(4): 331337.
［12］MAULIK UJJWAL, BANDYOPADHYAY SANGHAM ITA. Genetic algorithmbased clustering technique［J］. Pattern Recognition, 2000, 33(9): 14551465.
［13］谷文祥, 李向涛, 朱磊, 等. 求解流水线调度问题的万有引力搜索算法［J］. 智能系统学报, 2010, 5(5): 411418.
?GU Wenxiang, LI Xiangtao, ZHU Lei, et al. A gravitational search algorithm for flow shop scheduling［J］. CAAI Transactions on Intelligent Systems, 2010, 5(5): 411418.
［14］ASUNCION A, NEWMAN D J. UCI machine learning repository［DB/OL］. Irvine, USA: University of California,School of Information and Computer Science, 2007.
- 相似文献/References:
-
[1]张佳骕,蒋亦樟,王士同.基于特征选择聚类方法的稀疏TSK模糊系统[J].智能系统学报,2015,10(4):583.[doi:10.3969/j.issn.1673-4785.201412001]
ZHANG Jiasu,JIANG Yizhang,WANG Shitong.Sparse TSK fuzzy system based on feature selection clustering method[J].CAAI Transactions on Intelligent Systems,2015,10():583.[doi:10.3969/j.issn.1673-4785.201412001]
[2]张永库,尹灵雪,孙劲光.基于改进的遗传算法的模糊聚类算法[J].智能系统学报,2015,10(4):627.[doi:10.3969/j.issn.1673-4785.201503033]
ZHANG Yongku,YIN Lingxue,SUN Jinguang.Fuzzy clustering algorithm based on the improved genetic algorithm[J].CAAI Transactions on Intelligent Systems,2015,10():627.[doi:10.3969/j.issn.1673-4785.201503033]
[3]郭瑛洁,王士同,许小龙.基于最大间隔理论的组合距离学习算法[J].智能系统学报,2015,10(6):843.[doi:10.11992/tis.201504027]
GUO Yingjie,WANG Shitong,XU Xiaolong.Learning a linear combination of distances based on the maximum-margin theory[J].CAAI Transactions on Intelligent Systems,2015,10():843.[doi:10.11992/tis.201504027]
[4]陈爱国,王士同.基于极大熵的知识迁移模糊聚类算法[J].智能系统学报,2017,12(1):95.[doi:10.11992/tis.201602003]
CHEN Aiguo,WANG Shitong.A maximum entropy-based knowledge transfer fuzzy clustering algorithm[J].CAAI Transactions on Intelligent Systems,2017,12():95.[doi:10.11992/tis.201602003]
[5]储德润,周治平.公理化模糊共享近邻自适应谱聚类算法[J].智能系统学报,2019,14(5):897.[doi:10.11992/tis.201810002]
CHU Derun,ZHOU Zhiping.Shared nearest neighbor adaptive spectral clustering algorithm based on axiomatic fuzzy set theory[J].CAAI Transactions on Intelligent Systems,2019,14():897.[doi:10.11992/tis.201810002]
[6]雷涛,王洁,薛丁华,等.差异特征融合的无监督SAR图像变化检测[J].智能系统学报,2021,16(3):595.[doi:10.11992/tis.202103011]
LEI Tao,WANG Jie,XUE Dinghua,et al.Unsupervised SAR image change detection based on difference feature fusion[J].CAAI Transactions on Intelligent Systems,2021,16():595.[doi:10.11992/tis.202103011]
[7]唐益明,陈仁好,李冰.面向模糊C均值算法的MAME聚类有效性指标[J].智能系统学报,2023,18(5):945.[doi:10.11992/tis.202212028]
TANG Yiming,CHEN Renhao,LI Bing.A clustering validity index called MAME for the fuzzy c-means algorithm[J].CAAI Transactions on Intelligent Systems,2023,18():945.[doi:10.11992/tis.202212028]
备注/Memo
收稿日期： 2011-06-15.
基金项目：国家自然科学基金资助项目（60803102, 61070084）.
通信作者：郭丽萍.E-mail：guolp281@nenu.edu.cn.
作者简介：
谷文祥,男,1947年生,教授,博士生导师,主要研究方向为智能规划与规划识别、形式语言与自动机理论、模糊数学及其应用.主持国家自然科学基金项目3项，发表学术论文100余篇.
郭丽萍,女,1989年生,硕士研究生,主要研究方向为智能规划、智能信息处理.
殷明浩,男,1979年生,副教授,主要研究方向为智能规划与自动推理.主持参与多项国家自然科学基金项目.发表学术论文30余篇,其中大部分被SCI和EI检索.
更新日期/Last Update:
2012-02-29