MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.
Cluster ensembles—a knowledge reuse framework for combining multiple partitions
2 Pith papers cite this work. Polarity classification is still indexing.
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Task-guided triplet selection via mutual information improves multi-annotation representation learning over static weighting on an aerial wildlife dataset.
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MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.
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Task-Guided Multi-Annotation Triplet Learning for Remote Sensing Representations
Task-guided triplet selection via mutual information improves multi-annotation representation learning over static weighting on an aerial wildlife dataset.