Abstract:Spectral clustering largely depends on the affinity graph, yet constructing a graph that preserves reliable local connectivity while adapting to heterogeneous data structures remains challenging. Existing granular-ball-based spectral clustering methods usually reduce graph complexity by using coarse-grained representatives. However, the learned local regions are often treated as graph nodes or anchors, and their structural information is not sufficiently used to regularize the original sample-level graph. To address this issue, this paper proposes a Minimum Description Length based Granular-Ball Tree-Regularized Spectral Clustering method, termed MDL-GBTRSC. The proposed method constructs a granular-ball tree through local MDL model selection, with reciprocal neighborhood continuity used to discourage splits that break reliable local connections. The stable leaf balls obtained from the tree provide coding-scale information for regularizing the sample-level affinity graph. In addition, a shared-neighbor bridge code is introduced to adjust weak local bridge relations without requiring an additional user-specified threshold. In this way, MDL-GBTRSC connects interpretable local representation learning with affinity graph construction in a unified spectral clustering framework. Experiments on real and synthetic datasets show that MDL-GBTRSC achieves the best average ARI and NMI under the adopted fixed-configuration protocol compared with classical spectral clustering baselines and representative granular-ball, micro-cluster, and anchor-based methods.
| Comments: | 28 pages, 5 figures, 6 tables |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.5.3 |
| Cite as: | arXiv:2605.22410 [cs.LG] |
| (or arXiv:2605.22410v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22410 arXiv-issued DOI via DataCite (pending registration) |
From: Caihui Liu [view email]
[v1]
Thu, 21 May 2026 12:36:24 UTC (3,066 KB)
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