Robust large-scale clustering based on correntropy.
With the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs to be solved urgently. Especially in the face of large-scale real-world data, which contains a large number of complex distributions of noises and outliers, the research...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277012 |
| _version_ | 1817975674199080960 |
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| author | Guodong Jin Jing Gao Lining Tan |
| author_facet | Guodong Jin Jing Gao Lining Tan |
| author_sort | Guodong Jin |
| collection | DOAJ |
| description | With the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs to be solved urgently. Especially in the face of large-scale real-world data, which contains a large number of complex distributions of noises and outliers, the research on robust large-scale real-world data clustering algorithms has become one of the hottest topics. In response to this issue, a robust large-scale clustering algorithm based on correntropy (RLSCC) is proposed in this paper, specifically, k-means is firstly applied to generated pseudo-labels which reduce input data scale of subsequent spectral clustering, then anchor graphs instead of full sample graphs are introduced into spectral clustering to obtain final clustering results based on pseudo-labels which further improve the efficiency. Therefore, RLSCC inherits the advantages of the effectiveness of k-means and spectral clustering while greatly reducing the computational complexity. Furthermore, correntropy is developed to suppress the influence of noises and outlier the real-world data on the robustness of clustering. Finally, extensive experiments were carried out on real-world datasets and noise datasets and the results show that compared with other state-of-the-art algorithms, RLSCC can improve efficiency and robustness greatly while maintaining comparable or even higher clustering effectiveness. |
| first_indexed | 2024-04-13T21:53:04Z |
| format | Article |
| id | doaj.art-c731a94781114624a12f7687cecca273 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T21:53:04Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c731a94781114624a12f7687cecca2732022-12-22T02:28:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711e027701210.1371/journal.pone.0277012Robust large-scale clustering based on correntropy.Guodong JinJing GaoLining TanWith the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs to be solved urgently. Especially in the face of large-scale real-world data, which contains a large number of complex distributions of noises and outliers, the research on robust large-scale real-world data clustering algorithms has become one of the hottest topics. In response to this issue, a robust large-scale clustering algorithm based on correntropy (RLSCC) is proposed in this paper, specifically, k-means is firstly applied to generated pseudo-labels which reduce input data scale of subsequent spectral clustering, then anchor graphs instead of full sample graphs are introduced into spectral clustering to obtain final clustering results based on pseudo-labels which further improve the efficiency. Therefore, RLSCC inherits the advantages of the effectiveness of k-means and spectral clustering while greatly reducing the computational complexity. Furthermore, correntropy is developed to suppress the influence of noises and outlier the real-world data on the robustness of clustering. Finally, extensive experiments were carried out on real-world datasets and noise datasets and the results show that compared with other state-of-the-art algorithms, RLSCC can improve efficiency and robustness greatly while maintaining comparable or even higher clustering effectiveness.https://doi.org/10.1371/journal.pone.0277012 |
| spellingShingle | Guodong Jin Jing Gao Lining Tan Robust large-scale clustering based on correntropy. PLoS ONE |
| title | Robust large-scale clustering based on correntropy. |
| title_full | Robust large-scale clustering based on correntropy. |
| title_fullStr | Robust large-scale clustering based on correntropy. |
| title_full_unstemmed | Robust large-scale clustering based on correntropy. |
| title_short | Robust large-scale clustering based on correntropy. |
| title_sort | robust large scale clustering based on correntropy |
| url | https://doi.org/10.1371/journal.pone.0277012 |
| work_keys_str_mv | AT guodongjin robustlargescaleclusteringbasedoncorrentropy AT jinggao robustlargescaleclusteringbasedoncorrentropy AT liningtan robustlargescaleclusteringbasedoncorrentropy |