Local neighbour spider monkey optimization algorithm for data clustering
Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Patel, Vaishali P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Evolutionary intelligence - Berlin : Springer, 2008, 16(2021), 1 vom: 08. Aug., Seite 133-151 |
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| Übergeordnetes Werk: |
volume:16 ; year:2021 ; number:1 ; day:08 ; month:08 ; pages:133-151 |
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| DOI / URN: |
10.1007/s12065-021-00647-1 |
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SPR049186469 |
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| 520 | |a Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. | ||
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| 650 | 4 | |a Chaotic factor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Clustering |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Rawat, Manoj Kumar |4 aut | |
| 700 | 1 | |a Patel, Amit S. |0 (orcid)0000-0003-3980-7744 |4 aut | |
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10.1007/s12065-021-00647-1 doi (DE-627)SPR049186469 (SPR)s12065-021-00647-1-e DE-627 ger DE-627 rakwb eng Patel, Vaishali P. verfasserin aut Local neighbour spider monkey optimization algorithm for data clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. Spider monkey optimization (dpeaa)DE-He213 Swarm optimization (dpeaa)DE-He213 Neighbour search (dpeaa)DE-He213 Chaotic factor (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rawat, Manoj Kumar aut Patel, Amit S. (orcid)0000-0003-3980-7744 aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 1 vom: 08. Aug., Seite 133-151 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:1 day:08 month:08 pages:133-151 https://dx.doi.org/10.1007/s12065-021-00647-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 1 08 08 133-151 |
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10.1007/s12065-021-00647-1 doi (DE-627)SPR049186469 (SPR)s12065-021-00647-1-e DE-627 ger DE-627 rakwb eng Patel, Vaishali P. verfasserin aut Local neighbour spider monkey optimization algorithm for data clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. Spider monkey optimization (dpeaa)DE-He213 Swarm optimization (dpeaa)DE-He213 Neighbour search (dpeaa)DE-He213 Chaotic factor (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rawat, Manoj Kumar aut Patel, Amit S. (orcid)0000-0003-3980-7744 aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 1 vom: 08. Aug., Seite 133-151 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:1 day:08 month:08 pages:133-151 https://dx.doi.org/10.1007/s12065-021-00647-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 1 08 08 133-151 |
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10.1007/s12065-021-00647-1 doi (DE-627)SPR049186469 (SPR)s12065-021-00647-1-e DE-627 ger DE-627 rakwb eng Patel, Vaishali P. verfasserin aut Local neighbour spider monkey optimization algorithm for data clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. Spider monkey optimization (dpeaa)DE-He213 Swarm optimization (dpeaa)DE-He213 Neighbour search (dpeaa)DE-He213 Chaotic factor (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rawat, Manoj Kumar aut Patel, Amit S. (orcid)0000-0003-3980-7744 aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 1 vom: 08. Aug., Seite 133-151 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:1 day:08 month:08 pages:133-151 https://dx.doi.org/10.1007/s12065-021-00647-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 1 08 08 133-151 |
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10.1007/s12065-021-00647-1 doi (DE-627)SPR049186469 (SPR)s12065-021-00647-1-e DE-627 ger DE-627 rakwb eng Patel, Vaishali P. verfasserin aut Local neighbour spider monkey optimization algorithm for data clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. Spider monkey optimization (dpeaa)DE-He213 Swarm optimization (dpeaa)DE-He213 Neighbour search (dpeaa)DE-He213 Chaotic factor (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rawat, Manoj Kumar aut Patel, Amit S. (orcid)0000-0003-3980-7744 aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 1 vom: 08. Aug., Seite 133-151 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:1 day:08 month:08 pages:133-151 https://dx.doi.org/10.1007/s12065-021-00647-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 1 08 08 133-151 |
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10.1007/s12065-021-00647-1 doi (DE-627)SPR049186469 (SPR)s12065-021-00647-1-e DE-627 ger DE-627 rakwb eng Patel, Vaishali P. verfasserin aut Local neighbour spider monkey optimization algorithm for data clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. Spider monkey optimization (dpeaa)DE-He213 Swarm optimization (dpeaa)DE-He213 Neighbour search (dpeaa)DE-He213 Chaotic factor (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rawat, Manoj Kumar aut Patel, Amit S. (orcid)0000-0003-3980-7744 aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 16(2021), 1 vom: 08. Aug., Seite 133-151 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:16 year:2021 number:1 day:08 month:08 pages:133-151 https://dx.doi.org/10.1007/s12065-021-00647-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2021 1 08 08 133-151 |
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Patel, Vaishali P. |
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Patel, Vaishali P. misc Spider monkey optimization misc Swarm optimization misc Neighbour search misc Chaotic factor misc Clustering Local neighbour spider monkey optimization algorithm for data clustering |
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Local neighbour spider monkey optimization algorithm for data clustering Spider monkey optimization (dpeaa)DE-He213 Swarm optimization (dpeaa)DE-He213 Neighbour search (dpeaa)DE-He213 Chaotic factor (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 |
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local neighbour spider monkey optimization algorithm for data clustering |
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Local neighbour spider monkey optimization algorithm for data clustering |
| abstract |
Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstractGer |
Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Local neighbour spider monkey optimization algorithm for data clustering |
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https://dx.doi.org/10.1007/s12065-021-00647-1 |
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Rawat, Manoj Kumar Patel, Amit S. |
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10.1007/s12065-021-00647-1 |
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2024-07-03T23:44:24.362Z |
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