An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation
Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarit...
Ausführliche Beschreibung
Autor*in: |
Yang, Qifen [verfasserIn] |
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2022 |
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 78(2022), 12 vom: 06. Apr., Seite 14597-14625 |
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Übergeordnetes Werk: |
volume:78 ; year:2022 ; number:12 ; day:06 ; month:04 ; pages:14597-14625 |
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DOI / URN: |
10.1007/s11227-022-04456-w |
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OLC2079184318 |
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520 | |a Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. | ||
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10.1007/s11227-022-04456-w doi (DE-627)OLC2079184318 (DE-He213)s11227-022-04456-w-p DE-627 ger DE-627 rakwb eng 004 620 VZ Yang, Qifen verfasserin aut An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. Spectral clustering Message passing Natural neighbors Affinity propagation Li, Ziyang (orcid)0000-0002-1647-0301 aut Han, Gang aut Gao, Wanyi aut Zhu, Shuhua aut Wu, Xiaotian aut Deng, Yuhui aut Enthalten in The journal of supercomputing Springer US, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2022 12 06 04 14597-14625 |
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10.1007/s11227-022-04456-w doi (DE-627)OLC2079184318 (DE-He213)s11227-022-04456-w-p DE-627 ger DE-627 rakwb eng 004 620 VZ Yang, Qifen verfasserin aut An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. Spectral clustering Message passing Natural neighbors Affinity propagation Li, Ziyang (orcid)0000-0002-1647-0301 aut Han, Gang aut Gao, Wanyi aut Zhu, Shuhua aut Wu, Xiaotian aut Deng, Yuhui aut Enthalten in The journal of supercomputing Springer US, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2022 12 06 04 14597-14625 |
allfields_unstemmed |
10.1007/s11227-022-04456-w doi (DE-627)OLC2079184318 (DE-He213)s11227-022-04456-w-p DE-627 ger DE-627 rakwb eng 004 620 VZ Yang, Qifen verfasserin aut An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. Spectral clustering Message passing Natural neighbors Affinity propagation Li, Ziyang (orcid)0000-0002-1647-0301 aut Han, Gang aut Gao, Wanyi aut Zhu, Shuhua aut Wu, Xiaotian aut Deng, Yuhui aut Enthalten in The journal of supercomputing Springer US, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2022 12 06 04 14597-14625 |
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10.1007/s11227-022-04456-w doi (DE-627)OLC2079184318 (DE-He213)s11227-022-04456-w-p DE-627 ger DE-627 rakwb eng 004 620 VZ Yang, Qifen verfasserin aut An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. Spectral clustering Message passing Natural neighbors Affinity propagation Li, Ziyang (orcid)0000-0002-1647-0301 aut Han, Gang aut Gao, Wanyi aut Zhu, Shuhua aut Wu, Xiaotian aut Deng, Yuhui aut Enthalten in The journal of supercomputing Springer US, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2022 12 06 04 14597-14625 |
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10.1007/s11227-022-04456-w doi (DE-627)OLC2079184318 (DE-He213)s11227-022-04456-w-p DE-627 ger DE-627 rakwb eng 004 620 VZ Yang, Qifen verfasserin aut An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. Spectral clustering Message passing Natural neighbors Affinity propagation Li, Ziyang (orcid)0000-0002-1647-0301 aut Han, Gang aut Gao, Wanyi aut Zhu, Shuhua aut Wu, Xiaotian aut Deng, Yuhui aut Enthalten in The journal of supercomputing Springer US, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2022 12 06 04 14597-14625 |
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Yang, Qifen Li, Ziyang Han, Gang Gao, Wanyi Zhu, Shuhua Wu, Xiaotian Deng, Yuhui |
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an improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation |
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An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation |
abstract |
Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation |
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Li, Ziyang Han, Gang Gao, Wanyi Zhu, Shuhua Wu, Xiaotian Deng, Yuhui |
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