Introduces the Perception algorithm for seed-guided semi-supervised clustering via a-contrario anomaly detection, defining clusters as anomaly-free subsets and achieving competitive performance with 10-30 seeds per cluster on benchmarks.
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Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
Introduces the Perception algorithm for seed-guided semi-supervised clustering via a-contrario anomaly detection, defining clusters as anomaly-free subsets and achieving competitive performance with 10-30 seeds per cluster on benchmarks.