Coupled Clustering: a Method for Detecting Structural Correspondence
Zvika Marx, Ido Dagan, Joachim Buhmann
Abstract
This paper proposes a new paradigm and computational framework for identification of correspondences between sub-structures of distinct composite systems. For this, we define and investigate a variant of traditional data clustering, termed coupled clustering, which simultaneously identifies corresponding clusters within two data sets. The presented method is demonstrated and evaluated for detecting topical correspondences in textual corpora.
Create a lesson
Related papers
Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols
Tariq Abdul-Quddoos, Xiangfang Li, Lijun Qian
Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation
Haocheng Xi, Yiming Xie, Hexu Zhao et al.
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan et al.
RISC-V and machine learning: a survey
Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo et al.
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sambit Mishra, Yingying Wang, Christine K. Johnson et al.
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Simon Süwer, Julian Klemm, Elisa Acitelli et al.