Abstract:Existing granular-ball generation methods are still mainly driven by handcrafted quality measures and heuristic splitting or stopping criteria, which weakens the transparency of local generation decisions in clustering. To address this issue, this paper proposes Minimum Description Length based Granular-Ball Generation (MDL-GBG), a non-parametric and interpretable granular-ball generation method for clustering. MDL-GBG reformulates granular-ball generation as a local model selection problem under the Minimum Description Length principle. For each granular ball, three candidate explanations are compared, namely a single-ball model, a two-ball model, and a core-ball-plus-residual model, and the model with the shortest description length is selected. In this way, ball retention, splitting, and residual peeling are unified within a common coding-theoretic framework. A residual reassignment mechanism is further introduced to globally re-evaluate peeled-off boundary samples after stable granular-balls are formed. Experiments on 20 UCI datasets show that the stable granular-balls generated by MDL-GBG provide a highly competitive upstream representation for clustering, with MDL-GBG+AC achieving the best overall average ranks in ARI, ACC, and NMI among the compared methods. These results demonstrate that MDL-GBG offers an effective and interpretable alternative to conventional heuristic granular-ball generation strategies.
| Comments: | 12 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.5.3; I.2.6 |
| Cite as: | arXiv:2605.08759 [cs.LG] |
| (or arXiv:2605.08759v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08759 arXiv-issued DOI via DataCite (pending registration) |
From: Caihui Liu [view email]
[v1]
Sat, 9 May 2026 07:42:41 UTC (737 KB)
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