Y-Graph: A Max-Ascent-Angle Graph for Detecting Clusters
Junyi Guan, Sheng Li, Xiongxiong He, Jiajia Chen, Yangyang Zhao, Yuxuan Zhang · IEEE Transactions on Knowledge and Data Engineering · 2024
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.