Constrained Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using hyperparameter optimization

Author:

Kim Jongwon,Lee Hyeseon,Ko Young MyoungORCID

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference58 articles.

1. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in: KDD, Vol. 96, 1996, pp. 226–231.

2. J. MacQueen, et al., Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, 1967, pp. 281–297.

3. Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data;Tian;Nature Commun.,2021

4. B. Wu, Y. Zhang, B.-G. Hu, Q. Ji, Constrained Clustering and Its Application to Face Clustering in Videos, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 3507–3514.

5. K. Wagstaff, C. Cardie, S. Rogers, S. Schrödl, Constrained K-means Clustering with Background Knowledge, in: Proceedings of the Eighteenth International Conference on Machine Learning, 2001, pp. 577–584.

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