JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 35-44.doi: 10.6040/j.issn.1671-9352.7.2023.343

Previous Articles     Next Articles

A neighbourhood granular fuzzy C-means clustering algorithm

ZHENG Chenying1, CHEN Yingyue1,2*, HOU Xianyu1, JIANG Lianji1, LIAO Liang1   

  1. 1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China;
    2. School of Economy and Management, Xiamen University of Technology, Xiamen 361024, Fujian, China
  • Published:2024-05-09

RichHTML

0

PDF (PC)

231

Abstract: Aiming at the problem that the sensitivity of initial value and noise lead to the decline of fuzzy C-means clustering, fuzzy C-means clustering method of neighborhood granule is proposed by introducing the theory of granular computation and using the neighborhood granulation technique. In the sample, the neighborhood granule is constructed by using the neighborhood granulation technique on single feature, and the neighborhood granular vector is formed by using granulation on multi-features.A variety of granule distance formulas are defined to measure the distance between granules. According to the granule distance measurement, a granular fuzzy C-means clustering method is proposed, and a granular fuzzy C-means clustering algorithm is designed. Multiple data sets are used to perform experiments, and the fuzzy C-means clustering algorithm is compared with the classical clustering algorithm. The results verify the feasibility and effectiveness of the proposed neighborhood granular fuzzy C-means clustering method.

Key words: granular computing, neighbourhood granules, fuzzy C-means clustering, unsupervised fuzzy clustering method, granule vectors

CLC Number: 

  • TP181
[1] ZADEH L A. Fuzzy sets and information granularity[J]. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, 1996, 8:433-448.
[2] 王国胤,张清华,胡军.粒计算研究综述[J]. 智能系统学报, 2007, 2(6):8-26. WANG Guoyin, ZHANG Qinghua, HU Jun. An overview of granular computing[J]. CAAI Transactions on Intelligent Systems, 2007, 2(6):8-26.
[3] LIN T Y. Granular computing on binary relations I:data mining and neighborhood systems[J]. Rough Sets in Knowledge Discovery, 1998, 1:2165-2166.
[4] 苗夺谦. Rough set理论及其在机器学习中的应用研究[D]. 北京: 中国科学院自动化研究所, 1997. MIAO Duoqian. Rough set theory and its application in machine learning[D]. Beijing: Institute of Automation, Chinese Academy of Sciences, 1997.
[5] 苗夺谦,王珏. 粗糙集理论中知识粗糙性与信息熵关系的讨论[J]. 模式识别与人工智能, 1998, 11(1):34-40. MIAO Duoqian, WANG Jue. Discussion on the relationship between knowledge roughness and information entropy in rough set theory[J]. Pattern Recognition and Artificial Intelligence, 1998, 11(1):34-40.
[6] 苗夺谦,王珏. 粗糙集理论中概念与运算的信息表示[J]. 软件学报, 1999, 10(2):113-116. MIAO Duoqian, WANG Jue. Information representation of concepts and operations in rough set theory [J].Journal of Software, 1999, 10(2):113-116.
[7] 陈祥焰,林耀进,王晨曦. 基于邻域粗糙集的高维类不平衡数据在线流特征选择[J]. 模式识别与人工智能, 2019, 32(8):726-735. CHEN Xiangyan, LIN Yaojin, WANG Chenxi. Online stream feature selection of high-dimensional unbalanced data based on neighborhood rough set[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(8):726-735.
[8] 白盛兴,林耀进,王晨曦,等. 基于邻域粗糙集的大规模层次分类在线流特征选择[J]. 模式识别与人工智能, 2019, 32(9):811-820. BAI Shengxing, LIN Yaojin, WANG Chenxi, et al. Large-scale hierarchical classification online streaming feature selection based on neighborhood rough set [J]. Pattern Recognition and Artificial Intelligence, 2019, 32(9):811-820.
[9] CHEN Yumin, ZHU Shunzhi, LI Wei, et al. Fuzzy granular convolutional classifiers[J].Fuzzy Sets and Systems, 2022, 426:145-162.
[10] 梁吉业,冯晨娇,宋鹏. 大数据相关分析综述[J]. 计算机学报, 2016, 39(1):1-18. LIANG Jiye, FENG Chenjiao, SONG Peng. Review of big data correlation analysis[J]. Chinese Journal of Computers, 2016, 39(1):1-18.
[11] 朱凡,王印琪. 基于k-means与神经网络机器学习算法的用户信息聚类及预测研究[J]. 情报科学, 2021, 39(7):83-90. ZHU Fan, WANG Yinqi. Research on user information clustering and predetection based on k-means and neural network machine learning algorithm[J]. Information Science, 2021, 39(7):83-90.
[12] 常思源,白晓征,刘君. 一种基于聚类分析的二维激波模式识别算法[J]. 航空学报, 2020, 41(8):162-175. CHANG Siyuan, BAI Xiaozheng, LIU Jun. A two-dimensional shock pattern recognition algorithm based on cluster analysis[J]. Acta Aeronautica et AstronauticaSinica, 2020, 41(8):162-175.
[13] 王海龙,柳林,林民,等. 基于信息检索及K均值聚类的音乐个性化推荐算法[J]. 吉林大学学报(工学版), 2021, 51(5):1845-1850. WANG Hailong, LIU Lin, LIN Ming, et al. Personalized music recommendation algorithm based on information retrieval and K-means clustering[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5):1845-1850.
[14] 张皓,吴建鑫. 基于深度特征的无监督图像检索研究综述[J]. 计算机研究与发展, 2018, 55(9):1829-1842. ZHANG Hao, WU Jianxin.A review of unsupervised image retrieval based on depth features[J].Journal of Computer Research and Development, 2018, 55(9):1829-1842.
[15] 凡嘉琛,王平心,杨习贝. 基于三支决策的密度敏感谱聚类[J]. 山东大学学报(理学版), 2023, 58(1):59-66. FAN Jiachen, WANG Pingxin, YANG Xibei. Density sensitive spectral clustering based on three branch decision [J]. Journal of Shandong University(Natural Science), 2023, 58(1):59-66.
[16] 陈健美,陆虎,宋余庆,等.一种隶属关系不确定的可能性模糊聚类方法[J]. 计算机研究与发展, 2008, 45(9):1486-1492. CHEN Jianmei, LU Hu, SONG Yuqin, et al. A possibility fuzzy clustering method with membership uncertainty[J].Journal of Computer Research and Development, 2008, 45(9):1486-1492.
[17] 李心雨,范辉,刘惊雷.基于自适应图调节和低秩矩阵分解的鲁棒聚类[J]. 山东大学学报(理学版), 2022, 57(8):21-38. LI Xinyu, FAN Hui, LIU Jinglei. Robust clustering based on adaptive graph adjustment and low rank Matrix decomposition[J]. Journal of Shandong University(Natural Science), 2022, 57(8):21-38.
[18] 杨婷,朱恒东,马盈仓,等. 基于L2,1范数和流形正则项的半监督谱聚类算法[J]. 山东大学学报(理学版), 2021, 56(3):67-76. YANG Ting, ZHU Hengdong, MA Yingcang, et al. Semi supervised spectral clustering algorithm based on L2,1 norm and manifold regularization term [J]. Journal of Shandong University(Natural Science), 2021, 56(3):67-76.
[19] 孙林,梁娜,徐久成.基于自适应邻域互信息与谱聚类的特征选择[J]. 山东大学学报(理学版), 2022, 57(12):13-24. SUN Lin, LIANG Na, XU Jiucheng. Feature selection based on adaptive neighborhood mutual information and spectral clustering [J]. Journal of Shandong University(Natural Science), 2022, 57(12):13-24.
[20] 傅兴宇,陈颖悦,陈玉明,等.一种全连接粒神经网络分类方法[J]. 山西大学学报(自然科学版), 2023, 46(1):91-100. FU Xingyu, CHEN Yingyue, CHEN Yuming, et al. A classification method of fully connected granular neural network[J].Journal of Shanxi University(Natural Science Edition), 2023, 46(1):91-100.
[21] 石文峰,商琳. 一种基于决策粗糙集的模糊C均值聚类数的确定方法[J]. 计算机科学, 2017, 44(9):45-48. SHI Wenfeng, SHANG Lin. A method to determine fuzzy C-means clustering number based on decision rough set[J]. Computer Science, 2017, 44(9):45-48.
[22] 王志豪.基于FCM的模糊聚类算法研究[D]. 厦门:厦门大学, 2019. WANG Zhihao. Research on fuzzy clustering algorithm based on FCM[D]. Xiamen: Xiamen University, 2019.
[23] BEZDEK J C, EHRLICH R, FULL W. FCM: the fuzzy C-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2/3):191-203.
[24] 陈玉明,蔡国强,卢俊文,等.一种邻域粒K均值聚类方法[J]. 控制与决策, 2023, 38(3):857-864. CHEN Yuming, CAI Guoqiang, LU Junwen, et al. A neighborhood K-means clustering method [J]. Control and Decision, 2023, 38(3):857-864.
[1] Fengqi FANG,Weizhi WU. Knowledge reduction in decision set-valued systems [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 82-89, 99.
[2] CHEN Yumin, ZHENG Guangyu, JIAO Na. Multi-label learning based on granular neural networks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 1-11.
[3] Jie TANG,Ling WEI,Rui-si REN,Si-yu ZHAO. Granule description using possible attribute analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2021, 56(1): 75-82.
[4] LI Jin-hai, HE Jian-jun, WU Wei-zhi. Optimization of class-attribute block in multi-granularity formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2020, 55(5): 1-12.
[5] LI Fen-ning, FAN Min, LI Jin-hai. Dynamic updating of object-oriented granular concepts in formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(4): 105-115.
[6] LI Jin-hai, WU Wei-zhi, DENG Shuo. Multi-scale theory in formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(2): 30-40.
[7] QIAN Ting, ZHAO Si-yu, HE Xiao-li. Rules acquisition of decision formal contexts based on attribute granular [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(10): 113-120.
[8] LI Li, GUAN Tao, LIN He. The hybrid parallel rough set model based on pansystems operators [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 22-29.
[9] LI Jin-hai, WU Wei-zhi. Granular computing approach for formal concept analysis and its research outlooks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 1-12.
[10] HUANG Wei-ting, ZHAO Hong, ZHU William. Adaptive divide and conquer algorithm for cost-sensitive attribute reduction [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(8): 98-104.
[11] MA Yuan-yuan, MENG Hui-li, XU Jiu-cheng, ZHU Ma. Normal distribution of lattice close-degree based on granular computing [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 107-110.
[12] TANG Ya-qiang, FAN Min, LI Jin-hai. Cognitive system model and approach to transformation of information granules under triadic formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 102-106.
[13] YANG Wei-ping1, LIN Meng-lei2. Information granularity in intuitionistic fuzzy information systems [J]. J4, 2012, 47(1): 87-92.
[14] ZHANG Qing-hua1,2, XING Yu-ke2, WANG Guo-yin2. Transformation between concept knowledge granule and concept information granule [J]. J4, 2010, 45(9): 1-6.
[15] QIU Tao-rong, WANG Lu, XIONG Shu-jie, BAI Xiao-ming. A granular computing approach for knowledge hiding [J]. J4, 2010, 45(7): 60-64.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] HUO Yu-hong, JI Quan-bao. Synchronization analysis of oscillatory activities in a biological cell system[J]. J4, 2010, 45(6): 105 -110 .
[2] SHI Chang-guang . Multi-soliton solution of the Faddeev model[J]. J4, 2007, 42(7): 38 -40 .
[3] MA Jie-xiong,JIANG Li,QI Yu-yu,XIANG FEng-ning,XIA Guang-min . The growth of calli and regenerated plantlets of Gentiana Przewalskii Maxim. and the constituents analysis of its two effective components[J]. J4, 2006, 41(6): 157 -160 .
[4] CHENG Zhi1,2, SUN Cui-fang2, WANG Ning1, DU Xian-neng1. On the fibre product of Zn and its property[J]. J4, 2013, 48(2): 15 -19 .
[5] ZHANG Ai-ping,LI Gang . LRquasinormalEhresmann semigroups[J]. J4, 2006, 41(5): 44 -47 .
[6] ZHAO Tong-xin1, LIU Lin-de1*, ZHANG Li1, PAN Cheng-chen2, JIA Xing-jun1. Pollinators and pollen polymorphism of  Wisteria sinensis (Sims) Sweet[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(03): 1 -5 .
[7] MA Jian-ling . Spectral characterization of the rhomb-type achromatic retarder[J]. J4, 2007, 42(7): 27 -29 .
[8] LI Yanhong. Generalized Egoroff theorems of sequence of fuzzy valued functions[J]. J4, 2009, 44(4): 88 -91 .
[9] YUE Jiang, CHANG Da-Wei. Preconditioned simultaneous displacement(PSD) method for rank deficient  linear systems[J]. J4, 2009, 44(10): 30 -35 .
[10] DONG Jiong, CAO Xiao-hong. Weyls theorem for the cube of operator and compact perturbations[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(8): 15 -21 .
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd