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What is Active Learning in Semi-Supervised Clustering about?
This paper presents ActSSDBSCAN, a novel active learning density-based clustering algorithm that integrates both seeds and pairwise constraints to enhance clustering performance. Experimental results demonstrate that ActSSDBSCAN outperforms the traditional seed-based DBSCAN algorithm, showcasing the effectiveness of combining seeds and constraints in semi-supervised clustering. The study highlights the potential of active learning mechanisms in minimizing expert solicitations while improving clustering accu
- Author
- WARSE Journals
- Language
- EN