Fuzzy K-means clustering with reconstructed information

Clustering techniques play a pivotal role in unveiling the inherent structure of unlabeled data. When dealing with overlapping clusters, traditional hard clustering methods encounter challenges. As a representative of soft clustering methods, Fuzzy K-Means (FKM) enables data points to be assigned di...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of machine learning and cybernetics Vol. 16; no. 1; pp. 43 - 53
Main Authors:	Huang, Honglan, Shi, Wei, Yang, Fangjie, Feng, Yanghe, Zhang, Longfei, Liang, Xingxing, Shi, Jun, Cheng, Guangquan, Huang, Jincai, Liu, Zhong
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Cluster analysis Clustering Complex Systems Computational Intelligence Control Data points Data structures Datasets Decision making Dimensional analysis Eigenvectors Engineering Mechatronics Methods Optimization Original Article Pattern Recognition Performance evaluation Robotics Systems Biology Vector quantization High-dimensional data Fuzzy K-means Alternating optimization algorithm Reconstructed information
ISSN:	1868-8071, 1868-808X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!