An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not...
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Gespeichert in:

Autor*in:	Xiaoqi He [verfasserIn] Sheng Zhang [verfasserIn] Yangguang Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors

Übergeordnetes Werk:	In: Algorithms - MDPI AG, 2008, 8(2015), 2, Seite 177-189
Übergeordnetes Werk:	volume:8 ; year:2015 ; number:2 ; pages:177-189

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/a8020177

Katalog-ID:	DOAJ022805087

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520			\|a The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.
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allfields	10.3390/a8020177 doi (DE-627)DOAJ022805087 (DE-599)DOAJafd39e13c1fd4705ad7340a4f1519a7e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Xiaoqi He verfasserin aut An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors Industrial engineering. Management engineering Electronic computers. Computer science Sheng Zhang verfasserin aut Yangguang Liu verfasserin aut In Algorithms MDPI AG, 2008 8(2015), 2, Seite 177-189 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:8 year:2015 number:2 pages:177-189 https://doi.org/10.3390/a8020177 kostenfrei https://doaj.org/article/afd39e13c1fd4705ad7340a4f1519a7e kostenfrei http://www.mdpi.com/1999-4893/8/2/177 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2015 2 177-189
spelling	10.3390/a8020177 doi (DE-627)DOAJ022805087 (DE-599)DOAJafd39e13c1fd4705ad7340a4f1519a7e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Xiaoqi He verfasserin aut An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors Industrial engineering. Management engineering Electronic computers. Computer science Sheng Zhang verfasserin aut Yangguang Liu verfasserin aut In Algorithms MDPI AG, 2008 8(2015), 2, Seite 177-189 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:8 year:2015 number:2 pages:177-189 https://doi.org/10.3390/a8020177 kostenfrei https://doaj.org/article/afd39e13c1fd4705ad7340a4f1519a7e kostenfrei http://www.mdpi.com/1999-4893/8/2/177 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2015 2 177-189
allfields_unstemmed	10.3390/a8020177 doi (DE-627)DOAJ022805087 (DE-599)DOAJafd39e13c1fd4705ad7340a4f1519a7e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Xiaoqi He verfasserin aut An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors Industrial engineering. Management engineering Electronic computers. Computer science Sheng Zhang verfasserin aut Yangguang Liu verfasserin aut In Algorithms MDPI AG, 2008 8(2015), 2, Seite 177-189 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:8 year:2015 number:2 pages:177-189 https://doi.org/10.3390/a8020177 kostenfrei https://doaj.org/article/afd39e13c1fd4705ad7340a4f1519a7e kostenfrei http://www.mdpi.com/1999-4893/8/2/177 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2015 2 177-189
allfieldsGer	10.3390/a8020177 doi (DE-627)DOAJ022805087 (DE-599)DOAJafd39e13c1fd4705ad7340a4f1519a7e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Xiaoqi He verfasserin aut An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors Industrial engineering. Management engineering Electronic computers. Computer science Sheng Zhang verfasserin aut Yangguang Liu verfasserin aut In Algorithms MDPI AG, 2008 8(2015), 2, Seite 177-189 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:8 year:2015 number:2 pages:177-189 https://doi.org/10.3390/a8020177 kostenfrei https://doaj.org/article/afd39e13c1fd4705ad7340a4f1519a7e kostenfrei http://www.mdpi.com/1999-4893/8/2/177 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2015 2 177-189
allfieldsSound	10.3390/a8020177 doi (DE-627)DOAJ022805087 (DE-599)DOAJafd39e13c1fd4705ad7340a4f1519a7e DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Xiaoqi He verfasserin aut An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors Industrial engineering. Management engineering Electronic computers. Computer science Sheng Zhang verfasserin aut Yangguang Liu verfasserin aut In Algorithms MDPI AG, 2008 8(2015), 2, Seite 177-189 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:8 year:2015 number:2 pages:177-189 https://doi.org/10.3390/a8020177 kostenfrei https://doaj.org/article/afd39e13c1fd4705ad7340a4f1519a7e kostenfrei http://www.mdpi.com/1999-4893/8/2/177 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2015 2 177-189
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source	In Algorithms 8(2015), 2, Seite 177-189 volume:8 year:2015 number:2 pages:177-189
sourceStr	In Algorithms 8(2015), 2, Seite 177-189 volume:8 year:2015 number:2 pages:177-189
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topic_facet	spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors Industrial engineering. Management engineering Electronic computers. Computer science
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container_title	Algorithms
authorswithroles_txt_mv	Xiaoqi He @@aut@@ Sheng Zhang @@aut@@ Yangguang Liu @@aut@@
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callnumber-first	T - Technology
author	Xiaoqi He
spellingShingle	Xiaoqi He misc T55.4-60.8 misc QA75.5-76.95 misc spectral clustering misc similarity measures misc Gaussian kernel function misc importance of nearest neighbors misc Industrial engineering. Management engineering misc Electronic computers. Computer science An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
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topic_title	T55.4-60.8 QA75.5-76.95 An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors spectral clustering similarity measures Gaussian kernel function importance of nearest neighbors
topic	misc T55.4-60.8 misc QA75.5-76.95 misc spectral clustering misc similarity measures misc Gaussian kernel function misc importance of nearest neighbors misc Industrial engineering. Management engineering misc Electronic computers. Computer science
topic_unstemmed	misc T55.4-60.8 misc QA75.5-76.95 misc spectral clustering misc similarity measures misc Gaussian kernel function misc importance of nearest neighbors misc Industrial engineering. Management engineering misc Electronic computers. Computer science
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title	An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
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title_full	An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
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title_sort	adaptive spectral clustering algorithm based on the importance of shared nearest neighbors
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title_auth	An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
abstract	The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.
abstractGer	The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.
abstract_unstemmed	The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.
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title_short	An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
url	https://doi.org/10.3390/a8020177 https://doaj.org/article/afd39e13c1fd4705ad7340a4f1519a7e http://www.mdpi.com/1999-4893/8/2/177 https://doaj.org/toc/1999-4893
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An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors

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