Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering
Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a c...
Ausführliche Beschreibung
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| Autor*in: |
Bharill, Neha [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Systems Assurance Engineering and Management - Springer-Verlag, 2010, 9(2017), 4 vom: 04. Nov., Seite 875-887 |
|---|---|
| Übergeordnetes Werk: |
volume:9 ; year:2017 ; number:4 ; day:04 ; month:11 ; pages:875-887 |
| Links: |
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| DOI / URN: |
10.1007/s13198-017-0681-x |
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SPR031294308 |
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| 520 | |a Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. | ||
| 650 | 4 | |a Quantum computing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fuzzy clustering |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Cluster validity index |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Patel, Om Prakash |4 aut | |
| 700 | 1 | |a Tiwari, Aruna |4 aut | |
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10.1007/s13198-017-0681-x doi (DE-627)SPR031294308 (SPR)s13198-017-0681-x-e DE-627 ger DE-627 rakwb eng Bharill, Neha verfasserin (orcid)0000-0002-2202-3647 aut Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. Quantum computing (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy c-Means (dpeaa)DE-He213 Cluster validity index (dpeaa)DE-He213 Patel, Om Prakash aut Tiwari, Aruna aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2017), 4 vom: 04. Nov., Seite 875-887 (DE-627)SPR031222420 nnns volume:9 year:2017 number:4 day:04 month:11 pages:875-887 https://dx.doi.org/10.1007/s13198-017-0681-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2017 4 04 11 875-887 |
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10.1007/s13198-017-0681-x doi (DE-627)SPR031294308 (SPR)s13198-017-0681-x-e DE-627 ger DE-627 rakwb eng Bharill, Neha verfasserin (orcid)0000-0002-2202-3647 aut Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. Quantum computing (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy c-Means (dpeaa)DE-He213 Cluster validity index (dpeaa)DE-He213 Patel, Om Prakash aut Tiwari, Aruna aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2017), 4 vom: 04. Nov., Seite 875-887 (DE-627)SPR031222420 nnns volume:9 year:2017 number:4 day:04 month:11 pages:875-887 https://dx.doi.org/10.1007/s13198-017-0681-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2017 4 04 11 875-887 |
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10.1007/s13198-017-0681-x doi (DE-627)SPR031294308 (SPR)s13198-017-0681-x-e DE-627 ger DE-627 rakwb eng Bharill, Neha verfasserin (orcid)0000-0002-2202-3647 aut Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. Quantum computing (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy c-Means (dpeaa)DE-He213 Cluster validity index (dpeaa)DE-He213 Patel, Om Prakash aut Tiwari, Aruna aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2017), 4 vom: 04. Nov., Seite 875-887 (DE-627)SPR031222420 nnns volume:9 year:2017 number:4 day:04 month:11 pages:875-887 https://dx.doi.org/10.1007/s13198-017-0681-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2017 4 04 11 875-887 |
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10.1007/s13198-017-0681-x doi (DE-627)SPR031294308 (SPR)s13198-017-0681-x-e DE-627 ger DE-627 rakwb eng Bharill, Neha verfasserin (orcid)0000-0002-2202-3647 aut Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. Quantum computing (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy c-Means (dpeaa)DE-He213 Cluster validity index (dpeaa)DE-He213 Patel, Om Prakash aut Tiwari, Aruna aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2017), 4 vom: 04. Nov., Seite 875-887 (DE-627)SPR031222420 nnns volume:9 year:2017 number:4 day:04 month:11 pages:875-887 https://dx.doi.org/10.1007/s13198-017-0681-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2017 4 04 11 875-887 |
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10.1007/s13198-017-0681-x doi (DE-627)SPR031294308 (SPR)s13198-017-0681-x-e DE-627 ger DE-627 rakwb eng Bharill, Neha verfasserin (orcid)0000-0002-2202-3647 aut Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. Quantum computing (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy c-Means (dpeaa)DE-He213 Cluster validity index (dpeaa)DE-He213 Patel, Om Prakash aut Tiwari, Aruna aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 9(2017), 4 vom: 04. Nov., Seite 875-887 (DE-627)SPR031222420 nnns volume:9 year:2017 number:4 day:04 month:11 pages:875-887 https://dx.doi.org/10.1007/s13198-017-0681-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 9 2017 4 04 11 875-887 |
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Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering |
| abstract |
Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 |
| abstractGer |
Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 |
| abstract_unstemmed |
Abstract Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like %$V_{CWB}%$ and %$V_{OS}%$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 |
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4 |
| title_short |
Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering |
| url |
https://dx.doi.org/10.1007/s13198-017-0681-x |
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true |
| author2 |
Patel, Om Prakash Tiwari, Aruna |
| author2Str |
Patel, Om Prakash Tiwari, Aruna |
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SPR031222420 |
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| doi_str |
10.1007/s13198-017-0681-x |
| up_date |
2024-07-03T23:02:26.188Z |
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1803600785507876864 |
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