Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 131973 |
| Fachzeitschrift | NEUROCOMPUTING |
| Jahrgang | 663 |
| Frühes Online-Datum | 4 Nov. 2025 |
| Publikationsstatus | Veröffentlicht - 28 Jan. 2026 |
Abstract
We propose a package called ORKM, which implements the ORKMC (Online Regularized K-Means Clustering) method for handling online multi-view or single-view data, which named ORKMeans function in the package incorporates a regularization term to address multi-view clustering problems with online updates. ORKM computes classification results, cluster center matrices, and view-specific weights for multi-view datasets. It also supports branching multi/single-view data by converting the online RKMC algorithm into an offline version, referred to as RKMC (Regularized K-Means Clustering) realized by function RKMeans. We demonstrate the package's functionality through simulations and real-world data analyses, comparing it with other methods and related R packages. Overall, ORKM exhibits stable performance and effective clustering results.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Neurowissenschaften (insg.)
- Kognitive Neurowissenschaft
- Informatik (insg.)
- Artificial intelligence
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in: NEUROCOMPUTING, Jahrgang 663, 131973, 28.01.2026.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - ORKM
T2 - An R package for online multi-view data clustering
AU - Yu, Miao
AU - Li, Shu
AU - Guo, Guangbao
N1 - Publisher Copyright: © 2025 Elsevier B.V.
PY - 2026/1/28
Y1 - 2026/1/28
N2 - We propose a package called ORKM, which implements the ORKMC (Online Regularized K-Means Clustering) method for handling online multi-view or single-view data, which named ORKMeans function in the package incorporates a regularization term to address multi-view clustering problems with online updates. ORKM computes classification results, cluster center matrices, and view-specific weights for multi-view datasets. It also supports branching multi/single-view data by converting the online RKMC algorithm into an offline version, referred to as RKMC (Regularized K-Means Clustering) realized by function RKMeans. We demonstrate the package's functionality through simulations and real-world data analyses, comparing it with other methods and related R packages. Overall, ORKM exhibits stable performance and effective clustering results.
AB - We propose a package called ORKM, which implements the ORKMC (Online Regularized K-Means Clustering) method for handling online multi-view or single-view data, which named ORKMeans function in the package incorporates a regularization term to address multi-view clustering problems with online updates. ORKM computes classification results, cluster center matrices, and view-specific weights for multi-view datasets. It also supports branching multi/single-view data by converting the online RKMC algorithm into an offline version, referred to as RKMC (Regularized K-Means Clustering) realized by function RKMeans. We demonstrate the package's functionality through simulations and real-world data analyses, comparing it with other methods and related R packages. Overall, ORKM exhibits stable performance and effective clustering results.
KW - Online K-means clustering
KW - Online multi-view data
KW - Regularization
KW - Single-view
UR - http://www.scopus.com/inward/record.url?scp=105021479888&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2025.131973
DO - 10.1016/j.neucom.2025.131973
M3 - Article
AN - SCOPUS:105021479888
VL - 663
JO - NEUROCOMPUTING
JF - NEUROCOMPUTING
SN - 0925-2312
M1 - 131973
ER -