南方科技大学知识苑(SUSTech KC): Robust $k$ -Means-Type Clustering for Noisy Data

题名	Robust $k$ -Means-Type Clustering for Noisy Data
作者	Xi Xiao 1,2; Hailong Ma 1; Guojun Gan 3; Qing Li 4,5 ; Bin Zhang 1; Shutao Xia 6
发表日期	2024
DOI	10.1109/TNNLS.2024.3392211
发表期刊	IEEE Transactions on Neural Networks and Learning Systems 影响因子和分区
ISSN	2162-2388
卷号	PP 期号:99
摘要	Data clustering is a fundamental machine learning task that seeks to categorize a dataset into homogeneous groups. However, real data usually contain noise, which poses significant challenges to clustering algorithms. In this article, motivated by how the $k$ -means algorithm is derived from a Gaussian mixture model (GMM), we propose a robust $k$ -means-type algorithm, named $k$ -means-type clustering based on $t$ -distribution (KMTD), by assuming that the data points are drawn from a special multivariate $t$ -mixture model (TMM). Compared to the Gaussian distribution, the $t$ -distribution has a fatter tail. The proposed algorithm is more robust to noise. Like the $k$ -means algorithm, the proposed algorithm is simpler than those based on a full TMM. Both synthetic and actual data are used to illustrate the proposed algorithm’s performance and efficiency. The experimental results demonstrated that the proposed algorithm operates more quickly than other sophisticated algorithms and, in most cases, achieves higher accuracy than the other algorithms.
相关链接	[IEEE记录]
收录类别	SCI ; EI
学校署名	其他
引用统计
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/778472
专题	南方科技大学
作者单位	1.Shenzhen International Graduate School, Tsinghua University, Shenzhen, China 2.Sichuan University, Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Chengdu, China 3.Department of Mathematics, University of Connecticut, Storrs, CT, USA 4.Future Network Research Institute, Southern University of Science and Technology, Shenzhen, China 5.Department of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory, Shenzhen, China 6.Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China
推荐引用方式 GB/T 7714	Xi Xiao,Hailong Ma,Guojun Gan,et al. Robust $k$ -Means-Type Clustering for Noisy Data[J]. IEEE Transactions on Neural Networks and Learning Systems,2024,PP(99).
APA	Xi Xiao,Hailong Ma,Guojun Gan,Qing Li,Bin Zhang,&Shutao Xia.(2024).Robust $k$ -Means-Type Clustering for Noisy Data.IEEE Transactions on Neural Networks and Learning Systems,PP(99).
MLA	Xi Xiao,et al."Robust $k$ -Means-Type Clustering for Noisy Data".IEEE Transactions on Neural Networks and Learning Systems PP.99(2024).