Projected fuzzy c-means clustering algorithm with instance penalty

doi:10.1016/j.eswa.2024.124563

	Projected fuzzy c-means clustering algorithm with instance penalty
	Wang, Jikui1 ; Wu, Yiwen2 ; Huang, Xueyan1 ; Zhang, Cuihong1 ; Nie, Feiping3
	2024-12-01
在线发表日期	2024-07
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
卷号	255
摘要	At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing highdimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c -means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c -means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n , which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm.
关键词	Dimensionality reduction Fuzzyc-means clustering Principal component analysis Instance penalty
DOI	10.1016/j.eswa.2024.124563
收录类别	SCIE ; EI
ISSN	0957-4174
语种	英语
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:001263552300001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
EI入藏号	20242716606098
EI主题词	Principal component analysis
EI分类号	723 Computer Software, Data Handling and Applications ; 802.2 Chemical Reactions ; 903.1 Information Sources and Analysis ; 922.2 Mathematical Statistics
原始文献类型	Article
EISSN	1873-6793
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/37550
专题	信息工程与人工智能学院
通讯作者	Wang, Jikui
作者单位	1.Lanzhou Univ Finance & Econ, Sch Informat Engn & Artificial Intelligence, Lanzhou 730000, Gansu, Peoples R China; 2.Dalian Univ Technol, Sch Econ & Management, Dalian 116024, Peoples R China; 3.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Shaanxi, Peoples R China
第一作者单位	信息工程与人工智能学院
通讯作者单位	信息工程与人工智能学院
推荐引用方式 GB/T 7714	Wang, Jikui,Wu, Yiwen,Huang, Xueyan,et al. Projected fuzzy c-means clustering algorithm with instance penalty[J]. EXPERT SYSTEMS WITH APPLICATIONS,2024,255.
APA	Wang, Jikui,Wu, Yiwen,Huang, Xueyan,Zhang, Cuihong,&Nie, Feiping.(2024).Projected fuzzy c-means clustering algorithm with instance penalty.EXPERT SYSTEMS WITH APPLICATIONS,255.
MLA	Wang, Jikui,et al."Projected fuzzy c-means clustering algorithm with instance penalty".EXPERT SYSTEMS WITH APPLICATIONS 255(2024).