A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability

Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jin Zhu [verfasserIn] Dongqin Jiang [verfasserIn] Pingxin Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2022)
Übergeordnetes Werk:	year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1155/2022/6555501

Katalog-ID:	DOAJ011933275

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520			\|a Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results.
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allfields	10.1155/2022/6555501 doi (DE-627)DOAJ011933275 (DE-599)DOAJ0e9de1f126024a7b8d8335c9c88319cb DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results. Engineering (General). Civil engineering (General) Mathematics Dongqin Jiang verfasserin aut Pingxin Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/article/0e9de1f126024a7b8d8335c9c88319cb kostenfrei http://dx.doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2022
spelling	10.1155/2022/6555501 doi (DE-627)DOAJ011933275 (DE-599)DOAJ0e9de1f126024a7b8d8335c9c88319cb DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results. Engineering (General). Civil engineering (General) Mathematics Dongqin Jiang verfasserin aut Pingxin Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/article/0e9de1f126024a7b8d8335c9c88319cb kostenfrei http://dx.doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2022
allfields_unstemmed	10.1155/2022/6555501 doi (DE-627)DOAJ011933275 (DE-599)DOAJ0e9de1f126024a7b8d8335c9c88319cb DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results. Engineering (General). Civil engineering (General) Mathematics Dongqin Jiang verfasserin aut Pingxin Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/article/0e9de1f126024a7b8d8335c9c88319cb kostenfrei http://dx.doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2022
allfieldsGer	10.1155/2022/6555501 doi (DE-627)DOAJ011933275 (DE-599)DOAJ0e9de1f126024a7b8d8335c9c88319cb DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results. Engineering (General). Civil engineering (General) Mathematics Dongqin Jiang verfasserin aut Pingxin Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/article/0e9de1f126024a7b8d8335c9c88319cb kostenfrei http://dx.doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2022
allfieldsSound	10.1155/2022/6555501 doi (DE-627)DOAJ011933275 (DE-599)DOAJ0e9de1f126024a7b8d8335c9c88319cb DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Jin Zhu verfasserin aut A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results. Engineering (General). Civil engineering (General) Mathematics Dongqin Jiang verfasserin aut Pingxin Wang verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2022) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2022 https://doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/article/0e9de1f126024a7b8d8335c9c88319cb kostenfrei http://dx.doi.org/10.1155/2022/6555501 kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 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 2022
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title	A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability
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title_auth	A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability
abstract	Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results.
abstractGer	Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results.
abstract_unstemmed	Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results.
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title_short	A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability
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