AI RESEARCH
MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
arXiv CS.LG
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ArXi:2605.08759v1 Announce Type: new 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.