吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (1): 246-251.doi: 10.13229/j.cnki.jdxbgxb201601037

Previous Articles     Next Articles

Kernel-based fuzzy C-means clustering method based on parameter optimization

LIU Yun, LIU Fu, HOU Tao, ZHANG Xiao   

  1. College of Communications Engineering, Jilin University, Changchun 130022, China
  • Received:2014-09-26 Online:2016-01-30 Published:2016-01-30

RICH HTML

1   

PDF (PC)

266

Abstract: Kernel-based Fuzzy C-means Clustering Method (KFCM) is a common method for data clustering. The performance of KFCM is greatly affected by the parameter of the kernel function, while the selection of kernel parameter is extremely difficult in practice. To solve this problem, a Parameter Optimization-based KFCM (POKFCM) is proposed according to the idea that the distances between samples of the same class are closer than the distance between samples from different classes. First, initial clustering of dataset is completed by K-means method. Then the optimal kernel parameter is determined by calculating the distance similarity between the actual kernel matrix and ideal kernel matrix. Finally, the optimal kernel parameter is applied to KFCM. Clustering experiment results of six UCI datasets illustrate that POKFCM can effectively improve the clustering performance of KFCM.

Key words: artificial intelligence, kernel-based fuzzy C-means, kernel function, parameter optimization

CLC Number: 

  • TP391
[1] Chen L, Lu M, Chen C L P, et al. Multiple kernel fuzzy C-means based image segmentation[C]∥IEEE International Conference on Systems, Man and Cybernetics, Istanbul,2010: 4123-4129.
[2] Gong M, Liang Y, Shi J, et al. Fuzzy C-means clustering with local information and kernel metric for image segmentation[J]. IEEE Transactions on Image Processing, 2013, 22(2): 573-584.
[3] Zhang J. Speech feature extraction of KPCA based on kernel fuzzy K-means clustering[C]∥IEEE International Conference on Computer Science and Service System (CSSS), Nanjing,2011: 756-759.
[4] 叶吉祥, 谭冠政, 路秋静. 基于核的非凸数据模糊C均值聚类研究[J]. 计算机工程与设计, 2005, 26 (7): 1784-1786.
Ye Ji-xiang, Tan Guan-zheng, Lu, Qiu-jing. Fuzzy C-means clustering algorithm to non-spherical shape data based on kernel[J]. Computer Engineering and Design, 2005, 26(7): 1784-1786.
[5] Park D C. Classification of audio signals using fuzzy C-means with divergence-based kernel[J]. Pattern Recognition Letters, 2009, 30 (9): 794-798.
[6] Liu J, Xu M. Kernelized fuzzy attribute C-means clustering algorithm[J]. Fuzzy Sets and Systems, 2008, 159 (18): 2428-2445.
[7] Gu C, Zhang S, Liu K, et al. Fuzzy kernel K-means clustering method based on immune genetic algorithm[J]. Journal of Computational Information Systems, 2011, 7 (1): 221-231.
[8] Mohamed B, Ahmed T, Lassad H, et al. A new extension of fuzzy C-Means algorithm using non Euclidean distance and kernel methods[C]∥International Conference on Control, Decision and Information Technologies (CoDIT), Hammamet, 2013: 242-249.
[9] Ferreira M R P, de Carvalho F D A T. Kernel fuzzy C-means with automatic variable weighting[J]. Fuzzy Sets and Systems, 2014, 237:1-46.
[10] Graves D, Pedrycz W. Fuzzy C-means, Gustafson-Kessel FCM, and kernel-based FCM: a comparative study[J]. Advances in Soft Computing, 2007,41:140-149.
[11] Graves D, Pedrycz W. Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study[J]. Fuzzy Sets and Systems, 2010, 161(4): 522-543.
[12] Chen B, Liu H, Bao Z. A kernel optimization method based on the localized kernel Fisher criterion[J]. Pattern Recognition, 2008, 41(3): 1098-1109.
[13] Li J B, Wang Y H, Chu S C, et al. Kernel self-optimization learning for kernel-based feature extraction and recognition[J]. Information Sciences, 2014, 257: 70-80.
[14] Na W, Xia L. Kernel parameter optimization for semi-supervised fuzzy clustering with pairwise constraints[J]. Chinese Journal of Electronics, 2008, 17(2): 2007-2010.
[15] 李晓宇, 张新峰, 沈兰荪. 一种确定径向基核函数参数的方法[J]. 电子学报, 2005, 33(12): 2459-2463.
Li Xiao-yu, Zhang Xin-feng, Shen Lan-sun. A selection means on the parameter of radius basis function[J]. Acta Electronica Sinica, 2005, 33 (12):2459-2463.
[16] Zhang H, Lu J. Semi-supervised fuzzy clustering: a kernel-based approach[J]. Knowledge-Based Systems, 2009, 22(6):477-481.
[1] DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.
[2] GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585.
[3] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] LI Jing, DING Ming-hui, LI Li-gang, CHEN Li-jun. Dynamic characteristics analysis and optimization of air spring based on the piston shape [J]. 吉林大学学报(工学版), 2018, 48(2): 355-363.
[9] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[10] YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[11] LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[12] MA Kai, GAO Ji-dong, YAN Lei, XU Tao. Dummy material parameter optimization based on calibration experiment of chest pressure in collision [J]. 吉林大学学报(工学版), 2017, 47(5): 1498-1503.
[13] QU Hui-yan, ZHAO Wei, QIN Ai-hong. A fast collision detection algorithm based on optimization operator [J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[14] LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[15] SHAO Ke-yong, CHEN Feng, WANG Ting-ting, WANG Ji-chi, ZHOU Li-peng. Full state based adaptive control of fractional order chaotic system without equilibrium point [J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd