Beyond k-Means++: Towards better cluster exploration with geometrical information

Author:

Ping YuanORCID,Li Huina,Hao Bin,Guo Chun,Wang Baocang

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference47 articles.

1. Ball k-means: Fast adaptive clustering with no bounds;Xia;IEEE Trans. Pattern Anal. Mach. Intell.,2022

2. FRSVC: Towards making support vector clustering consume less;Ping;Pattern Recogn.,2017

3. Top 10 algorithms in data mining;Wu;Knowl. Inf. Syst.,2008

4. D. Arthur, S. Vassilvitskii, k-means++: The Advantages of Careful Seeding, in: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’07, 2007, pp. 1027–1035.

5. A rapid hybrid clustering algorithm for large volumes of high dimensional data;Rathore;IEEE Trans. Knowl. Data Eng.,2019

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