Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets
Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wan, Renxia [verfasserIn] Gao, Yuelin [author] Li, Caixia [author] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
IGI Global InfoSci Journals Archive 2000 - 2012 |
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| Übergeordnetes Werk: |
In: International journal of data warehousing and mining - Hershey, Pa : IGI Global, 2005, 8(2012), 4, Seite 82-107 |
| Übergeordnetes Werk: |
volume:8 ; year:2012 ; number:4 ; pages:82-107 |
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| DOI / URN: |
10.4018/jdwm.2012100104 |
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| 520 | |a Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes | ||
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10.4018/jdwm.2012100104 doi (DE-627)NLEJ244463018 (VZGNL)10.4018/jdwm.2012100104 DE-627 ger DE-627 rakwb eng Wan, Renxia verfasserin aut Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes IGI Global InfoSci Journals Archive 2000 - 2012 C-Means Fuzzy C-Means Fuzzy Possibilitic Large Data Set Weighted Cluster Weighted Fuzzy-Possibilitic C-Means Gao, Yuelin author aut Li, Caixia author aut In International journal of data warehousing and mining Hershey, Pa : IGI Global, 2005 8(2012), 4, Seite 82-107 Online-Ressource (DE-627)NLEJ244418896 (DE-600)2399996-2 1548-3932 nnns volume:8 year:2012 number:4 pages:82-107 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 8 2012 4 82-107 |
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10.4018/jdwm.2012100104 doi (DE-627)NLEJ244463018 (VZGNL)10.4018/jdwm.2012100104 DE-627 ger DE-627 rakwb eng Wan, Renxia verfasserin aut Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes IGI Global InfoSci Journals Archive 2000 - 2012 C-Means Fuzzy C-Means Fuzzy Possibilitic Large Data Set Weighted Cluster Weighted Fuzzy-Possibilitic C-Means Gao, Yuelin author aut Li, Caixia author aut In International journal of data warehousing and mining Hershey, Pa : IGI Global, 2005 8(2012), 4, Seite 82-107 Online-Ressource (DE-627)NLEJ244418896 (DE-600)2399996-2 1548-3932 nnns volume:8 year:2012 number:4 pages:82-107 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 8 2012 4 82-107 |
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10.4018/jdwm.2012100104 doi (DE-627)NLEJ244463018 (VZGNL)10.4018/jdwm.2012100104 DE-627 ger DE-627 rakwb eng Wan, Renxia verfasserin aut Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes IGI Global InfoSci Journals Archive 2000 - 2012 C-Means Fuzzy C-Means Fuzzy Possibilitic Large Data Set Weighted Cluster Weighted Fuzzy-Possibilitic C-Means Gao, Yuelin author aut Li, Caixia author aut In International journal of data warehousing and mining Hershey, Pa : IGI Global, 2005 8(2012), 4, Seite 82-107 Online-Ressource (DE-627)NLEJ244418896 (DE-600)2399996-2 1548-3932 nnns volume:8 year:2012 number:4 pages:82-107 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 8 2012 4 82-107 |
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10.4018/jdwm.2012100104 doi (DE-627)NLEJ244463018 (VZGNL)10.4018/jdwm.2012100104 DE-627 ger DE-627 rakwb eng Wan, Renxia verfasserin aut Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes IGI Global InfoSci Journals Archive 2000 - 2012 C-Means Fuzzy C-Means Fuzzy Possibilitic Large Data Set Weighted Cluster Weighted Fuzzy-Possibilitic C-Means Gao, Yuelin author aut Li, Caixia author aut In International journal of data warehousing and mining Hershey, Pa : IGI Global, 2005 8(2012), 4, Seite 82-107 Online-Ressource (DE-627)NLEJ244418896 (DE-600)2399996-2 1548-3932 nnns volume:8 year:2012 number:4 pages:82-107 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 8 2012 4 82-107 |
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10.4018/jdwm.2012100104 doi (DE-627)NLEJ244463018 (VZGNL)10.4018/jdwm.2012100104 DE-627 ger DE-627 rakwb eng Wan, Renxia verfasserin aut Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes IGI Global InfoSci Journals Archive 2000 - 2012 C-Means Fuzzy C-Means Fuzzy Possibilitic Large Data Set Weighted Cluster Weighted Fuzzy-Possibilitic C-Means Gao, Yuelin author aut Li, Caixia author aut In International journal of data warehousing and mining Hershey, Pa : IGI Global, 2005 8(2012), 4, Seite 82-107 Online-Ressource (DE-627)NLEJ244418896 (DE-600)2399996-2 1548-3932 nnns volume:8 year:2012 number:4 pages:82-107 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2012100104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 8 2012 4 82-107 |
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| abstract |
Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes |
| abstractGer |
Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes |
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Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes |
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Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets |
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