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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	International journal of machine learning and cybernetics Ročník 16; číslo 1; s. 43 - 53
Hlavní autoři:	Huang, Honglan, Shi, Wei, Yang, Fangjie, Feng, Yanghe, Zhang, Longfei, Liang, Xingxing, Shi, Jun, Cheng, Guangquan, Huang, Jincai, Liu, Zhong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!