Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization

doi:10.1007/s12204-015-1605-z

	Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization
	Zhao, Xiao-qiang; Zhou, Jin-hu
刊名	Journal of Shanghai Jiaotong University (Science)
	2015-04-02
卷号	20 期号:2 页码:164-170
关键词	Copying Data mining Fuzzy clustering Fuzzy systems Fuzzy C mean Fuzzy C-means algorithms Fuzzy c-means clustering algorithms High-dimensional feature space Initial clustering centers Invasive weed optimization Objection functions University of California
ISSN号	10071172
DOI	10.1007/s12204-015-1605-z
英文摘要	Fuzzy c-means (FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means (PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization (IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization (IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine (UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm. © 2015, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.
语种	英语
出版者	Shanghai Jiao Tong University, 2200 Xietu Rd no.25,, Shanghai, 200032, China
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/112380]
专题	电气工程与信息工程学院
作者单位	College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Zhao, Xiao-qiang,Zhou, Jin-hu. Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization[J]. Journal of Shanghai Jiaotong University (Science),2015,20(2):164-170.
APA	Zhao, Xiao-qiang,&Zhou, Jin-hu.(2015).Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization.Journal of Shanghai Jiaotong University (Science),20(2),164-170.
MLA	Zhao, Xiao-qiang,et al."Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization".Journal of Shanghai Jiaotong University (Science) 20.2(2015):164-170.