A new swarm-based efficient data clustering approach using KHM and fuzzy logic
Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gupta, Yogesh [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 23(2018), 1 vom: 10. Sept., Seite 145-162 |
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| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:1 ; day:10 ; month:09 ; pages:145-162 |
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| DOI / URN: |
10.1007/s00500-018-3514-1 |
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| 520 | |a Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses. | ||
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10.1007/s00500-018-3514-1 doi (DE-627)OLC2034894286 (DE-He213)s00500-018-3514-1-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Gupta, Yogesh verfasserin aut A new swarm-based efficient data clustering approach using KHM and fuzzy logic 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses. Clustering Fuzzy logic Particle swarm optimization K-harmonic means -measure Saini, Ashish aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 1 vom: 10. Sept., Seite 145-162 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:1 day:10 month:09 pages:145-162 https://doi.org/10.1007/s00500-018-3514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 1 10 09 145-162 |
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10.1007/s00500-018-3514-1 doi (DE-627)OLC2034894286 (DE-He213)s00500-018-3514-1-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Gupta, Yogesh verfasserin aut A new swarm-based efficient data clustering approach using KHM and fuzzy logic 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses. Clustering Fuzzy logic Particle swarm optimization K-harmonic means -measure Saini, Ashish aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 1 vom: 10. Sept., Seite 145-162 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:1 day:10 month:09 pages:145-162 https://doi.org/10.1007/s00500-018-3514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 1 10 09 145-162 |
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Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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A new swarm-based efficient data clustering approach using KHM and fuzzy logic |
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https://doi.org/10.1007/s00500-018-3514-1 |
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Saini, Ashish |
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