Grid-DPC: Improved density peaks clustering based on spatial grid walk-Reference-Cited by-同舟云学术

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Grid-DPC: Improved density peaks clustering based on spatial grid walk

Published:2022-05-25 Issue:3 Volume:53 Page:3221-3239
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Liang Bo,Cai JiangHui^ORCID,Yang HaiFeng

Funder

Graduate Education Innovation Project of Shanxi Province

National Natural Science Foundation of China

Key Research and Development Projects of Shanxi Province

Central Government Guides Local Science and Technology Development Funds

ShanXi Science and Technology Department

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03705-y.pdf

Reference52 articles.

1. Dogan A, Birant D (2021) Machine learning and data mining in manufacturing. Expert Syst Appl 166:114060. https://doi.org/10.1016/j.eswa.2020.114060

2. Maia J, Severiano C, Guimarães F, Castro C, Lemos A, Galindo J, Weiss M (2020) Evolving clustering algorithm based on mixture of typicalities for stream data mining. Future Generation Computer Systems, 106. https://doi.org/10.1016/j.future.2020.01.017

3. Adolfsson A, Ackerman M, Brownstein NC (2019) To cluster, or not to cluster: an analysis of clusterability methods. Pattern Recogn 88:13–26. https://doi.org/10.1016/j.patcog.2018.10.026

4. Chauhan R, Batra P, Chaudhary S (2014) A survey of density based clustering algorithms. www.ijcst.com 5. https://doi.org/10.1007/s11704-019-9059-

5. Zaman M, Hassan A (2019) Improved statistical features-based control chart patterns recognition using anfis with fuzzy clustering. Neural Comput Appl 31:5935–5949. https://doi.org/10.1007/s00521-018-3388-2

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