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
Gespeichert in:
| Autor*in: |
Yang, Qifen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© 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 - Dordrecht [u.a.] : Springer Science + Business Media B.V, 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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SPR047628839 |
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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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| 650 | 4 | |a Message passing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Natural neighbors |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Affinity propagation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, Ziyang |0 (orcid)0000-0002-1647-0301 |4 aut | |
| 700 | 1 | |a Han, Gang |4 aut | |
| 700 | 1 | |a Gao, Wanyi |4 aut | |
| 700 | 1 | |a Zhu, Shuhua |4 aut | |
| 700 | 1 | |a Wu, Xiaotian |4 aut | |
| 700 | 1 | |a Deng, Yuhui |4 aut | |
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10.1007/s11227-022-04456-w doi (DE-627)SPR047628839 (SPR)s11227-022-04456-w-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Message passing (dpeaa)DE-He213 Natural neighbors (dpeaa)DE-He213 Affinity propagation (dpeaa)DE-He213 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 Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://dx.doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 12 06 04 14597-14625 |
| spelling |
10.1007/s11227-022-04456-w doi (DE-627)SPR047628839 (SPR)s11227-022-04456-w-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Message passing (dpeaa)DE-He213 Natural neighbors (dpeaa)DE-He213 Affinity propagation (dpeaa)DE-He213 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 Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://dx.doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 12 06 04 14597-14625 |
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10.1007/s11227-022-04456-w doi (DE-627)SPR047628839 (SPR)s11227-022-04456-w-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Message passing (dpeaa)DE-He213 Natural neighbors (dpeaa)DE-He213 Affinity propagation (dpeaa)DE-He213 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 Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://dx.doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 12 06 04 14597-14625 |
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10.1007/s11227-022-04456-w doi (DE-627)SPR047628839 (SPR)s11227-022-04456-w-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Message passing (dpeaa)DE-He213 Natural neighbors (dpeaa)DE-He213 Affinity propagation (dpeaa)DE-He213 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 Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://dx.doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 12 06 04 14597-14625 |
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10.1007/s11227-022-04456-w doi (DE-627)SPR047628839 (SPR)s11227-022-04456-w-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Message passing (dpeaa)DE-He213 Natural neighbors (dpeaa)DE-He213 Affinity propagation (dpeaa)DE-He213 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 Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 12 vom: 06. Apr., Seite 14597-14625 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:12 day:06 month:04 pages:14597-14625 https://dx.doi.org/10.1007/s11227-022-04456-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 12 06 04 14597-14625 |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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Yang, Qifen |
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Yang, Qifen misc Spectral clustering misc Message passing misc Natural neighbors misc Affinity propagation 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 Spectral clustering (dpeaa)DE-He213 Message passing (dpeaa)DE-He213 Natural neighbors (dpeaa)DE-He213 Affinity propagation (dpeaa)DE-He213 |
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misc Spectral clustering misc Message passing misc Natural neighbors misc Affinity propagation |
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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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| title_short |
An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation |
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https://dx.doi.org/10.1007/s11227-022-04456-w |
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Li, Ziyang Han, Gang Gao, Wanyi Zhu, Shuhua Wu, Xiaotian Deng, Yuhui |
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Li, Ziyang Han, Gang Gao, Wanyi Zhu, Shuhua Wu, Xiaotian Deng, Yuhui |
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10.1007/s11227-022-04456-w |
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2024-07-03T13:57:55.680Z |
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| score |
7.4018297 |