Automatically Determining the Number of Clusters in Unlabeled Data Sets-Reference-Cited by-同舟云学术

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Automatically Determining the Number of Clusters in Unlabeled Data Sets

Published:2009-03 Issue:3 Volume:21 Page:335-350
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Wang Liang,Leckie Christopher,Ramamohanarao Kotagiri,Bezdek James

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx5/69/4759625/04759628.pdf?arnumber=4759628

Cited by 69 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. HaVAT: Automatic Cluster Structure Assessment in Unlabeled data;Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD);2024-01-04

2. ML-aVAT: A Novel 2-Stage Machine-Learning Approach for Automatic Clustering Tendency Assessment;Big Data Research;2023-11

3. DeepVAT: A Self-Supervised Technique for Cluster Assessment in Image Datasets;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

4. A parallel algorithm for approximating the silhouette using a ball tree;Journal of Parallel and Distributed Computing;2023-03

5. Finding the Number of Clusters Using a Small Training Sequence;IEEE Access;2023

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