Automating DBSCAN via Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Automating DBSCAN via Deep Reinforcement Learning

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
language:
Short-container-title:

Author:

Zhang Ruitong¹,Peng Hao¹,Dou Yingtong²,Wu Jia³,Sun Qingyun¹,Li Yangyang⁴,Zhang Jingyi¹,Yu Philip S.²

Affiliation:

1. Beihang University, Beijing, China

2. University of Illinois Chicago, Chicago, IL, USA

3. Macquarie University, Sydney, Australia

4. NERC-RPP, CAEIT, Beijing, China

Funder

National Key Research and Development Program of China

Science and Technology Program of Hebei Province

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557245

Reference58 articles.

1. 2022. scikit-opt. https://github.com/guofei9987/scikit-opt 2022. scikit-opt. https://github.com/guofei9987/scikit-opt

2. Automated Determination of the Input Parameter of DBSCAN Based on Outlier Detection

3. Mihael Ankerst , Markus M Breunig , Hans-Peter Kriegel , and Jörg Sander . 1999 . OPTICS: Ordering points to identify the clustering structure. ACM Sigmod record , Vol. 28 , 2 (1999), 49--60. Mihael Ankerst, Markus M Breunig, Hans-Peter Kriegel, and Jörg Sander. 1999. OPTICS: Ordering points to identify the clustering structure. ACM Sigmod record, Vol. 28, 2 (1999), 49--60.

4. Abraham Bagherjeiran , Christoph F Eick , and Ricardo Vilalta . 2005. Adaptive clustering: Better representatives with reinforcement learning. Department of Computer Science , University of Houston , Houston ( 2005 ). Abraham Bagherjeiran, Christoph F Eick, and Ricardo Vilalta. 2005. Adaptive clustering: Better representatives with reinforcement learning. Department of Computer Science, University of Houston, Houston (2005).

5. James Bergstra , Rémi Bardenet , Yoshua Bengio , and Balázs Kégl . 2011. Algorithms for hyper-parameter optimization. Advances in neural information processing systems , Vol. 24 ( 2011 ). James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. Algorithms for hyper-parameter optimization. Advances in neural information processing systems, Vol. 24 (2011).

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lightweight graph neural network architecture search based on heuristic algorithms;International Journal of Machine Learning and Cybernetics;2024-09-04

2. Synthetic data for learning-based knowledge discovery;ACM SIGKDD Explorations Newsletter;2024-07-24

3. A reinforcement learning-based metaheuristic algorithm for on-demand ride-pooling;2024 International Conference on Intelligent Environments (IE);2024-06-17

4. Enhancing fairness of trading environment: discovering overlapping spammer groups with dynamic co-review graph optimization;Cybersecurity;2024-06-04

5. Improving world models for robot arm grasping with backward dynamics prediction;International Journal of Machine Learning and Cybernetics;2024-04-07