2018/12/08 by Yuka Yoneda, Yoneda, Yuka, Mahito Sugiyama +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Biomedical Text Mining and Ontologies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1812.03395
openalex publication_date 2018/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a novel method that can learn a graph representation from multivariate data. In our representation, each node represents a cluster of data points and each edge represents the subset-superset relationship between clusters, which can be mutually overlapped. The key to our method is to use formal concept analysis (FCA), which can extract hierarchical relationships between clusters based on the algebraic closedness property. We empirically show that our method can effectively extract hierarchical structures of clusters compared to the baseline method.