Enhancing Big Data Conversion Validation with Alpha-Lightweight Coreset-Reference-Cited by-同舟云学术

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Enhancing Big Data Conversion Validation with Alpha-Lightweight Coreset

Published:2023-10-14 Issue:6 Volume:4 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Hoang Nguyen Le^ORCID,Dang Tran Khanh

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s42979-023-02206-0.pdf

Reference47 articles.

1. Agarwal PK, Procopiuc CM, Varadarajan KR. Approximating extent measures of points. J ACM (JACM). 2004;51(4):606–35.

2. Agarwal PK, Procopiuc CM, Varadarajan KR. Geometric approximation via coresets. Combin Comp Geom. 2005;52:1–30.

3. Arora S. Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems. J Assoc Comput Mach. 1998;45(5):753–82.

4. Arthur D, Vassilvitskii S. k-Means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM Symposium on Discrete Algorithms. 2007;1027–35.

5. Ackermann K, Angus SD. A resource efficient big data analysis method for the social sciences: the case of global IP activity. Procedia Comput Sci. 2014;29:2360–9.

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