Gaussian-kernel c-means clustering algorithms
Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inac...
Ausführliche Beschreibung
Autor*in: |
Chang-Chien, Shou-Jen [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 25(2020), 3 vom: 15. Apr., Seite 1699-1716 |
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Übergeordnetes Werk: |
volume:25 ; year:2020 ; number:3 ; day:15 ; month:04 ; pages:1699-1716 |
Links: |
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DOI / URN: |
10.1007/s00500-020-04924-6 |
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Katalog-ID: |
OLC2123661279 |
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520 | |a Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation. | ||
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10.1007/s00500-020-04924-6 doi (DE-627)OLC2123661279 (DE-He213)s00500-020-04924-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Chang-Chien, Shou-Jen verfasserin aut Gaussian-kernel c-means clustering algorithms 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation. Clustering Hard -means (HCM) Fuzzy -means (FCM) Gaussian-kernel HCM (GK-HCM) Gaussian-kernel FCM (GK-FCM) MRI segmentation Nataliani, Yessica aut Yang, Miin-Shen aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2020), 3 vom: 15. Apr., Seite 1699-1716 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2020 number:3 day:15 month:04 pages:1699-1716 https://doi.org/10.1007/s00500-020-04924-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2020 3 15 04 1699-1716 |
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10.1007/s00500-020-04924-6 doi (DE-627)OLC2123661279 (DE-He213)s00500-020-04924-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Chang-Chien, Shou-Jen verfasserin aut Gaussian-kernel c-means clustering algorithms 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation. Clustering Hard -means (HCM) Fuzzy -means (FCM) Gaussian-kernel HCM (GK-HCM) Gaussian-kernel FCM (GK-FCM) MRI segmentation Nataliani, Yessica aut Yang, Miin-Shen aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2020), 3 vom: 15. Apr., Seite 1699-1716 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2020 number:3 day:15 month:04 pages:1699-1716 https://doi.org/10.1007/s00500-020-04924-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2020 3 15 04 1699-1716 |
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Gaussian-kernel c-means clustering algorithms |
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Chang-Chien, Shou-Jen |
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Soft computing |
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Chang-Chien, Shou-Jen Nataliani, Yessica Yang, Miin-Shen |
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Chang-Chien, Shou-Jen |
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10.1007/s00500-020-04924-6 |
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004 |
title_sort |
gaussian-kernel c-means clustering algorithms |
title_auth |
Gaussian-kernel c-means clustering algorithms |
abstract |
Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
abstractGer |
Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
abstract_unstemmed |
Abstract Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For solving these drawbacks in HCM and FCM, Wu and Yang (Pattern Recognit 35:2267–2278, 2002) proposed the alternative c-means clustering with an exponential-type distance that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we construct a more generalization of AHCM and AFCM with Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. For theoretical behaviors of GK-FCM, we analyze the bordered Hessian matrix and then give the theoretical properties of the GK-FCM algorithm. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness. Finally, we apply the GK-FCM algorithm to MRI segmentation. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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title_short |
Gaussian-kernel c-means clustering algorithms |
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https://doi.org/10.1007/s00500-020-04924-6 |
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Nataliani, Yessica Yang, Miin-Shen |
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up_date |
2024-07-03T19:21:20.525Z |
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