Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble
Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clu...
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Gespeichert in:
| Autor*in: |
Ma, Tinghuai [verfasserIn] Yu, Te [verfasserIn] Wu, Xiuge [verfasserIn] Cao, Jie [verfasserIn] Al-Abdulkarim, Alia [verfasserIn] Al-Dhelaan, Abdullah [verfasserIn] Al-Dhelaan, Mohammed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 24(2020), 20 vom: 20. Aug., Seite 15129-15141 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2020 ; number:20 ; day:20 ; month:08 ; pages:15129-15141 |
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| DOI / URN: |
10.1007/s00500-020-05264-1 |
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SPR041020332 |
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| 520 | |a Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. | ||
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| 700 | 1 | |a Al-Dhelaan, Mohammed |e verfasserin |4 aut | |
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10.1007/s00500-020-05264-1 doi (DE-627)SPR041020332 (SPR)s00500-020-05264-1-e DE-627 ger DE-627 rakwb eng Ma, Tinghuai verfasserin aut Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. Selective clustering ensemble (dpeaa)DE-He213 Clustering solution (dpeaa)DE-He213 Multiple clustering and selecting algorithms (dpeaa)DE-He213 Combining strategy (dpeaa)DE-He213 Yu, Te verfasserin aut Wu, Xiuge verfasserin aut Cao, Jie verfasserin aut Al-Abdulkarim, Alia verfasserin aut Al-Dhelaan, Abdullah verfasserin aut Al-Dhelaan, Mohammed verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2020), 20 vom: 20. Aug., Seite 15129-15141 (DE-627)SPR006469531 nnns volume:24 year:2020 number:20 day:20 month:08 pages:15129-15141 https://dx.doi.org/10.1007/s00500-020-05264-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2020 20 20 08 15129-15141 |
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10.1007/s00500-020-05264-1 doi (DE-627)SPR041020332 (SPR)s00500-020-05264-1-e DE-627 ger DE-627 rakwb eng Ma, Tinghuai verfasserin aut Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. Selective clustering ensemble (dpeaa)DE-He213 Clustering solution (dpeaa)DE-He213 Multiple clustering and selecting algorithms (dpeaa)DE-He213 Combining strategy (dpeaa)DE-He213 Yu, Te verfasserin aut Wu, Xiuge verfasserin aut Cao, Jie verfasserin aut Al-Abdulkarim, Alia verfasserin aut Al-Dhelaan, Abdullah verfasserin aut Al-Dhelaan, Mohammed verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2020), 20 vom: 20. Aug., Seite 15129-15141 (DE-627)SPR006469531 nnns volume:24 year:2020 number:20 day:20 month:08 pages:15129-15141 https://dx.doi.org/10.1007/s00500-020-05264-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2020 20 20 08 15129-15141 |
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10.1007/s00500-020-05264-1 doi (DE-627)SPR041020332 (SPR)s00500-020-05264-1-e DE-627 ger DE-627 rakwb eng Ma, Tinghuai verfasserin aut Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. Selective clustering ensemble (dpeaa)DE-He213 Clustering solution (dpeaa)DE-He213 Multiple clustering and selecting algorithms (dpeaa)DE-He213 Combining strategy (dpeaa)DE-He213 Yu, Te verfasserin aut Wu, Xiuge verfasserin aut Cao, Jie verfasserin aut Al-Abdulkarim, Alia verfasserin aut Al-Dhelaan, Abdullah verfasserin aut Al-Dhelaan, Mohammed verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2020), 20 vom: 20. Aug., Seite 15129-15141 (DE-627)SPR006469531 nnns volume:24 year:2020 number:20 day:20 month:08 pages:15129-15141 https://dx.doi.org/10.1007/s00500-020-05264-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2020 20 20 08 15129-15141 |
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10.1007/s00500-020-05264-1 doi (DE-627)SPR041020332 (SPR)s00500-020-05264-1-e DE-627 ger DE-627 rakwb eng Ma, Tinghuai verfasserin aut Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. Selective clustering ensemble (dpeaa)DE-He213 Clustering solution (dpeaa)DE-He213 Multiple clustering and selecting algorithms (dpeaa)DE-He213 Combining strategy (dpeaa)DE-He213 Yu, Te verfasserin aut Wu, Xiuge verfasserin aut Cao, Jie verfasserin aut Al-Abdulkarim, Alia verfasserin aut Al-Dhelaan, Abdullah verfasserin aut Al-Dhelaan, Mohammed verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2020), 20 vom: 20. Aug., Seite 15129-15141 (DE-627)SPR006469531 nnns volume:24 year:2020 number:20 day:20 month:08 pages:15129-15141 https://dx.doi.org/10.1007/s00500-020-05264-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2020 20 20 08 15129-15141 |
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10.1007/s00500-020-05264-1 doi (DE-627)SPR041020332 (SPR)s00500-020-05264-1-e DE-627 ger DE-627 rakwb eng Ma, Tinghuai verfasserin aut Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. Selective clustering ensemble (dpeaa)DE-He213 Clustering solution (dpeaa)DE-He213 Multiple clustering and selecting algorithms (dpeaa)DE-He213 Combining strategy (dpeaa)DE-He213 Yu, Te verfasserin aut Wu, Xiuge verfasserin aut Cao, Jie verfasserin aut Al-Abdulkarim, Alia verfasserin aut Al-Dhelaan, Abdullah verfasserin aut Al-Dhelaan, Mohammed verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2020), 20 vom: 20. Aug., Seite 15129-15141 (DE-627)SPR006469531 nnns volume:24 year:2020 number:20 day:20 month:08 pages:15129-15141 https://dx.doi.org/10.1007/s00500-020-05264-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2020 20 20 08 15129-15141 |
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Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. |
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Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. |
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Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR041020332</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201126032437.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-020-05264-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR041020332</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-020-05264-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ma, Tinghuai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Selective clustering ensemble</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering solution</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiple clustering and selecting algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Combining strategy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Te</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Xiuge</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Jie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Abdulkarim, Alia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Dhelaan, Abdullah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Dhelaan, Mohammed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">24(2020), 20 vom: 20. Aug., Seite 15129-15141</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:24</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:20</subfield><subfield code="g">day:20</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:15129-15141</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-020-05264-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">24</subfield><subfield code="j">2020</subfield><subfield code="e">20</subfield><subfield code="b">20</subfield><subfield code="c">08</subfield><subfield code="h">15129-15141</subfield></datafield></record></collection>
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