A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but t...
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Autor*in:	Zhenzhen He [verfasserIn] Zongpu Jia [verfasserIn] Xiaohong Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	clustering number of clusters gaussian kernel inter-center distance

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 11(2019), 1, p 16
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:1, p 16

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info11010016

Katalog-ID:	DOAJ009274995

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520			\|a Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.
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spelling	10.3390/info11010016 doi (DE-627)DOAJ009274995 (DE-599)DOAJff3a54bcc79948ff9ee1cf175f8ea9eb DE-627 ger DE-627 rakwb eng T58.5-58.64 Zhenzhen He verfasserin aut A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm. clustering number of clusters gaussian kernel inter-center distance Information technology Zongpu Jia verfasserin aut Xiaohong Zhang verfasserin aut In Information MDPI AG, 2010 11(2019), 1, p 16 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:11 year:2019 number:1, p 16 https://doi.org/10.3390/info11010016 kostenfrei https://doaj.org/article/ff3a54bcc79948ff9ee1cf175f8ea9eb kostenfrei https://www.mdpi.com/2078-2489/11/1/16 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 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 11 2019 1, p 16
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allfieldsSound	10.3390/info11010016 doi (DE-627)DOAJ009274995 (DE-599)DOAJff3a54bcc79948ff9ee1cf175f8ea9eb DE-627 ger DE-627 rakwb eng T58.5-58.64 Zhenzhen He verfasserin aut A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm. clustering number of clusters gaussian kernel inter-center distance Information technology Zongpu Jia verfasserin aut Xiaohong Zhang verfasserin aut In Information MDPI AG, 2010 11(2019), 1, p 16 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:11 year:2019 number:1, p 16 https://doi.org/10.3390/info11010016 kostenfrei https://doaj.org/article/ff3a54bcc79948ff9ee1cf175f8ea9eb kostenfrei https://www.mdpi.com/2078-2489/11/1/16 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 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 11 2019 1, p 16
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callnumber-first	T - Technology
author	Zhenzhen He
spellingShingle	Zhenzhen He misc T58.5-58.64 misc clustering misc number of clusters misc gaussian kernel misc inter-center distance misc Information technology A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point
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topic_title	T58.5-58.64 A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point clustering number of clusters gaussian kernel inter-center distance
topic	misc T58.5-58.64 misc clustering misc number of clusters misc gaussian kernel misc inter-center distance misc Information technology
topic_unstemmed	misc T58.5-58.64 misc clustering misc number of clusters misc gaussian kernel misc inter-center distance misc Information technology
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title	A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point
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title_sort	fast method for estimating the number of clusters based on score and the minimum distance of the center point
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title_auth	A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point
abstract	Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.
abstractGer	Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.
abstract_unstemmed	Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.
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title_short	A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point
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score	7.3994074

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A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

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