Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs
Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning. Among various clustering methods, hierarchical clustering, density-based clustering, and graph clustering stand out as representative approaches. For hierarchical clustering, it can be categorized into agglomerative and divisive modes to construct clusters in a recursive manner. The key aspect of both modes is the calculation of inter-cluster similarity, which determines whether to merge the sub-clusters into one cluster or divide a current cluster into sub-clusters. Traditionally, the similarity is derived from pairwise distances, often overlooking density variations and structural co...
| Authors | Yuning Yu, José Rodríguez-Piñeiro, Xuefeng Yin et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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