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Y-Graph: A Max-Ascent-Angle Graph for Detecting Clusters
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DOI：10.1109/TKDE.2024.3486221.png)
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摘要
En 中文
Graph clustering technique is highly effective in detecting complex-shaped clusters, in which graph building is a crucial step. Nevertheless, building a reasonable graph that can exhibit high connectivity within clusters and low connectivity across clusters is challenging. Herein, we design a max-ascent-angle graph called the Y-graph, a high-sparse graph that automatically allocates dense edges within clusters and sparse edges across clusters, regardless of their shapes or dimensionality. In the graph, every point x is allowed to connect its nearest higher-density neighbor delta, and another higher-density neighbor gamma, satisfying that the angle angle delta x gamma is the largest, called max-ascent-angle. By seeking the max-ascent-angle, points are automatically connected as the Y-graph, which is a reasonable graph that can effectively balance inter-cluster connectivity and intra-cluster non-connectivity. Besides, an edge weight function is designed to capture the similarity of the neighbor probability distribution, which effectively represents the density connectivity between points. By employing the Normalized-Cut (Ncut) technique, a Ncut-Y algorithm is proposed. Benefiting from the excellent performance of Y-graph, Ncut-Y can fast seek and cut the edges located in the low-density boundaries between clusters, thereby, capturing clusters effectively. Experimental results on both synthetic and real datasets demonstrate the effectiveness of Y-graph and Ncut-Y.
Keyword:
Clustering algorithms
Merging
Buildings
Shape
Couplings
Resource management
Costs
Tuning
Symmetric matrices
Standards
Complex-shaped clusters
normalized-cut
graph clustering
期刊
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10.4
论文数:
6.9K
被引数:
3.2W
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