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Ensemble clustering based on evidence extracted from the co-association matrix

  • Published:2019-08 Issue: Volume:92 Page:93-106
  • ISSN:0031-3203
  • Container-title:Pattern Recognition
  • language:en
  • Short-container-title:Pattern Recognition

Author:

Zhong CaimingORCID,Hu Lianyu,Yue Xiaodong,Luo Ting,Fu Qiang,Xu Haiyong

Funder

Natural Science Foundation of China

https://doi.org/10.13039/501100004387

Publisher

Elsevier BV

Subject

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

Reference55 articles.

1. To cluster, or not to cluster;Adolfsson;An analysis of clusterability methods, Pattern Recognition,2019

2. Fast density clustering strategies based on the k-means algorithm;Bai;Pattern Recognit.,2017

3. Subspace clustering guided unsupervised feature selection;Zhu;Pattern Recognit.,2017

4. A novel clustering method based on hybrid k-nearest-neighbor graph;Qin;Pattern Recognit.,2018

5. Multi-view low-rank sparse subspace clustering;Brbić;Pattern Recognit.,2018

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2. Clustering Methods for Multidimensional Data from Social Media;2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon);2024-04-25

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4. Algorithm 1038: KCC: A MATLAB Package for k -Means-based Consensus Clustering;ACM Transactions on Mathematical Software;2023-12-15

5. Bidirectional Weighted Co-Association-Based Clustering Ensemble Algorithm for High-Speed Train Delay Prediction;Computational and Experimental Simulations in Engineering;2023-12-05
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