🤖 AI Summary
This work addresses the limitations of traditional hierarchical clustering, which relies solely on pairwise distances and struggles to capture density variations and local connectivity inherent in graph-structured data. The authors propose a novel integrated approach that synergistically combines hierarchical, density-based, and graph clustering paradigms. Specifically, they construct KNN-induced subgraphs and introduce a cluster similarity measure that jointly accounts for local density—estimated via kernel density estimation—and graph connectivity, enabling recursive generation of the clustering hierarchy. A key advantage of this method is its ability to automatically infer intrinsic thresholds, thereby substantially reducing reliance on manual parameter tuning. Extensive experiments on diverse heterogeneous benchmark datasets demonstrate that the proposed algorithm consistently outperforms state-of-the-art methods such as AChameleon and RNN-DBSCAN in both clustering accuracy and parameter robustness.
📝 Abstract
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 connectivity in graphs. To address this, we propose a density-aware hierarchical clustering method based on element-categorized connection subgraphs (DHC-ECS), which effectively integrates the hierarchical clustering, density-based clustering, and graph clustering. Particularly, a novel inter-cluster similarity metric is introduced that considers not only distances but also the element categorization in the KNN connection subgraphs, kernel density estimation, and local connectivity within sub-clusters. Extensive evaluations on heterogeneous benchmark datasets demonstrate that DHC-ECS exhibits superior overall performance in terms of clustering accuracy and parameter robustness compared with the baseline methods (including AChameleon, RNN-DBSCAN, McDPC, and G-RMS). The work indicates the great potential of the proposed clustering algorithm for low-dimensional datasets by leveraging local density and graph-structured connectivity (i.e., the duality of vertices and edges), as well as the possibility to determine an intrinsic threshold, reducing the reliance on manual parameter tuning.