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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 74-82.doi: 10.6040/j.issn.1672-3961.0.2022.268

• 机器学习与数据挖掘 • 上一篇    

变异萤火虫优化的粗糙K-均值聚类算法

李兆彬1,叶军1,2,周浩岩1,卢岚1,谢立1   

  1. 1. 南昌工程学院信息工程学院, 江西 南昌 330000;2. 江西省水信息协同感知与智能处理重点实验室(南昌工程学院), 江西 南昌 330000
  • 发布日期:2023-08-18
  • 作者简介:李兆彬(1998— ),男,河南安阳人,硕士研究生,主要研究方向为粗糙集与粒计算理论、聚类算法、群智能算法. E-mail:1012782202@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61562061);江西省教育厅科技项目(GJJ211920、GJJ170995)

A rough K-means clustering algorithm optimized by mutation firefly algorithm

LI Zhaobin1, YE Jun1,2, ZHOU Haoyan1, LU Lan1, XIE Li1   

  1. 1. College of Information Engineering, Nanchang Institute of Engineering, Nanchang 330000, Jiangxi, China;
    2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing(Nanchang Institute of Engineering), Nanchang 330000, Jiangxi, China
  • Published:2023-08-18

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摘要： 粗糙K-means聚类算法存在如下不足:随机选取初始聚类中心会导致算法过早收敛容易陷入局部最优,质心更新公式中的权重和决定对象划分的阈值采用定值有时会导致聚类结果波动较大和精度下降。针对以上问题,引入一种变异策略和差分进化的萤火虫算法,从3个方面进行优化:构造新的目标函数,以目标函数值作为萤火虫光亮强度进行初始聚类中心点的搜索,把萤火虫算法求得的最优解作为算法的聚类中心进行聚类迭代;以下近似集和边界集中对象数量的变化以及对象分布的差异性动态调整质心权重;给出一种通过迭代次数自动获取阈值的方法。试验结果表明,改进后的算法减少了迭代次数,聚类结果稳定性好,准确率更高,改善了算法对随机初始中心点的敏感和稳定性不足等问题。

关键词: K-means聚类算法, 萤火虫算法, 变异, 粗糙集

中图分类号: 

  • TP391
[1] HATHAWAY R J, BEZDEK J C. Switching regression models and fuzzy clustering[J]. IEEE Transactions on Fuzzy Systems, 1993, 1(3): 195-204.
[2] KRISHNAPURAM R, KELLER J M. A possibilistic approach to clustering[J]. IEEE Transactions on Fuzzy Systems, 1993, 1(2): 98-110.
[3] PAWLAK Z. Rough sets[J]. International Journal of Information and Computer Sciences, 1982, 11(5): 341-356.
[4] LINGRAS P, WEST C. Interval set clustering of web users with rough K-means[J]. Journal of Intelligent Information Systems, 2004, 23(1): 5-16.
[5] 谢娟英,张琰,谢维信,等.一种新的密度加权粗糙K-均值聚类算法[J].山东大学学报(理学版),2010,45(7): 1-6. XIE Juanying, ZHANG Yan, XIE Weixin, et al. A novel rough K-means clustering algorithm based on the weight of density[J]. Journal of Shangdong University(Natural Science), 2010, 45(7): 1-6.
[6] 段文影,李向军,邱桃荣,等. 一种具有自适应参数的基于密度加权的粗糙K-均值算法[J]. 南昌大学学报(理科版), 2012,36(5): 498-501. DUAN Wenying, LI Xiangjun, QIU Taorong, et al. Rough K-means clustering algorithm with self-adaptive parameter and weighted-density[J]. Journal of Nanchang University(Natural Science), 2012, 36(5): 498-501.
[7] KHAN H S, AHMAD A. Cluster center initialization algorithm for K-means clustering[J]. Pattern Recognition Letter, 2004, 25( 11): 1293-1302.
[8] 马福民,孙静勇,张腾飞.考虑边界样本邻域归属信息的粗糙K-means增量聚类算法[J].控制与决策,2022,37(11):2968-2976. MA Fumin, SUN Jingyong, ZHANG Tengfei. A rough K-means incremental clustering algorithm considering neighborhood attribution information of boundary samples [J]. Control and Decision, 2022, 37(11):2968-2976.
[9] 李艳,范斌,郭劼,等.基于K-原型聚类和粗糙集的属性约简方法[J].计算机科学,2021,48(增刊1):342-348. LI Yan, FAN Bin, GUO Jie, et al. Attribute reduction method based on K-prototype clustering and rough sets[J]. Computer Science, 2021, 48(Suppl.1):342-348.
[10] 王子龙,李进,宋亚飞.基于距离和权重改进的K-means算法[J].计算机工程与应用,2020,56(23):87-94. WANG Zilong, LI Jin, SONG Yafei. Improved K-means algorithm based on distance and weight[J]. Computer Engineering and Applications,2020,56(23):87-94.
[11] 洪亮亮,罗可.改进的基于遗传算法的粗糙聚类方法[J].计算机工程与应用,2010,46(25):142-145. HONG Liangliang, LUO Ke. Improved rough clustering method based on genetic algorithm[J]. Computer Engineering and Applications, 2010, 46(25):142-145.
[12] 汤文亮,张平,汤树芳.基于精英反向学习的萤火虫K-means改进算法[J].计算机工程与设计, 2019, 40(11):3164-3169. TANG Wenliang, ZHANG Ping, TANG Shufang. An improved firefly K-means algorithm based on elite reverse learning[J]. Computer Engineering and Design,2019,40(11):3164-3169.
[13] 刘洋,王慧琴,张小红.结合蚁群算法的改进粗糙K均值聚类算法[J].数据采集与处理,2019,34(2):341-348. LIU Yang, WANG Huiqin, ZHANG Xiaohong. An improved rough K means clustering algorithm combined with ant colony algorithm[J]. Journal of Data Acquisition and Processing, 2019, 34(2):341-348.
[14] 叶廷宇,叶军,王晖,等.结合人工蜂群优化的粗糙K-means聚类算法[J].计算机科学与探索,2022,16(8):1923-1932. YE Tingyu, YE Jun, WANG Hui, et al. Rough K-means clustering algorithm combined with artificial bee colony optimization[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8):1923-1932.
[15] KUMAR V, KUMAR D. A systematic review on firefly algorithm: past, present, and future[J]. Archives of Computational Methods in Engineering, 2021, 28(4): 3269-3291.
[16] CUI Z, CHANG Y, ZHANG J, et al. Improved NSGA-III with selection-and-elimination operator[J]. Swarm and Evolutionary Computation, 2019, 49: 23-33.
[17] PAN L, HE C, TIAN Y, et al. A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2018, 23(1): 74-88.
[18] CUI Z, CAO Y, CAI X, et al. Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things[J]. Journal of Parallel and Distributed Computing, 2019, 132: 217-229.
[19] SENTHILNATH J, OMKAR S N, MANI V. Clustering using firefly algorithm: performance study[J]. Swarm and Evolutionary Computation, 2011, 1(3): 164-171.
[20] 王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246. WANG Guoyin, YAO Yiyu, YU Hong. A review of rough set theory and application[J].Chinese Journal of Computers, 2009, 32(7):1229-1246.
[21] PETERS G. Outliers in rough K-means clustering[C] //International Conference on Pattern Recognition and Machine Intelligence. Berlin, Germany:Springer, 2005: 702-707.
[22] PETERS G. Some refinements of rough K-means clustering[J]. Pattern Recognition, 2006, 39(8): 1481-1491.
[23] 马福民,逯瑞强,张腾飞.基于局部密度自适应度量的粗糙K-means聚类算法[J].计算机工程与科学, 2018,40(1):184-190. MA Fumin, LU Ruiqiang, ZHANG Tengfei. Rough K-means clustering algorithm based on adaptive measure of local density[J].Computer Engineering & Science, 2018, 40(1):184-190.
[24] YANG X S. Firefly algorithms for multimodal optimization[C] / / International Symposium on Stochastic Algorithms. Berlin, Germany: Springer, 2009: 169-178.
[25] HASSAN B A. CSCF: a chaotic sine cosine firefly algorithm for practical application problems[J]. Neural Computing and Applications, 2021, 33(12): 7011-7030.
[26] 张大力,夏红伟,张朝兴,等.改进萤火虫算法及其收敛性分析[J].系统工程与电子技术,2022,44(4):1291-1300. ZHANG Dali, XIA Hongwei, ZHANG Chaoxing, et al. Improved firefly algorithm and its convergence analysis[J]. Systems Engineering and Electronics, 2022, 44(4):1291-1300.
[27] 周涛.具有自适应参数的粗糙K-means聚类算法[J].计算机工程与应用,2010,46(26):7-10. ZHOU Tao. A rough K-means clustering algorithm with adaptive parameters[J]. Computer Engineering and Applications, 2010, 46(26):7-10.
[28] 李莲,罗可,周博翔.基于粒计算的粗糙集聚类算法[J].计算机应用研究,2013,30(10):2916-2919. LI Lian, LUO Ke, ZHOU Boxiang. Rough clustering algorithm based on particle computing[J]. Application Research of Computers, 2013, 30(10):2916-2919.
[29] PETERS G. Rough clustering utilizing the principle of indifference[J]. Information Sciences, 2014, 277: 358-374.
[30] 周杨,苗夺谦,岳晓冬.基于自适应权重的粗糙K均值聚类算法[J].计算机科学,2011,38(6):237-241. ZHOU Yang, MIAO Duoqian, YUE Xiaodong. Rough K-means clustering algorithm based on adaptive weights[J]. Computer Science, 2011, 38(6):237-241.
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