Entropy-type classification maximum likelihood algorithms for mixture models
Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better res...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lai, Chien-Yo [verfasserIn] Yang, Miin-Shen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 15(2010), 2 vom: 19. Feb., Seite 373-381 |
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| Übergeordnetes Werk: |
volume:15 ; year:2010 ; number:2 ; day:19 ; month:02 ; pages:373-381 |
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| DOI / URN: |
10.1007/s00500-010-0560-8 |
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| 520 | |a Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. | ||
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10.1007/s00500-010-0560-8 doi (DE-627)SPR00647845X (SPR)s00500-010-0560-8-e DE-627 ger DE-627 rakwb eng Lai, Chien-Yo verfasserin aut Entropy-type classification maximum likelihood algorithms for mixture models 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. Classification maximum likelihood (CML) (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy CML (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Entropy-type CML (dpeaa)DE-He213 Parameter-free (dpeaa)DE-He213 Yang, Miin-Shen verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 2 vom: 19. Feb., Seite 373-381 (DE-627)SPR006469531 nnns volume:15 year:2010 number:2 day:19 month:02 pages:373-381 https://dx.doi.org/10.1007/s00500-010-0560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 2 19 02 373-381 |
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10.1007/s00500-010-0560-8 doi (DE-627)SPR00647845X (SPR)s00500-010-0560-8-e DE-627 ger DE-627 rakwb eng Lai, Chien-Yo verfasserin aut Entropy-type classification maximum likelihood algorithms for mixture models 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. Classification maximum likelihood (CML) (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy CML (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Entropy-type CML (dpeaa)DE-He213 Parameter-free (dpeaa)DE-He213 Yang, Miin-Shen verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 2 vom: 19. Feb., Seite 373-381 (DE-627)SPR006469531 nnns volume:15 year:2010 number:2 day:19 month:02 pages:373-381 https://dx.doi.org/10.1007/s00500-010-0560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 2 19 02 373-381 |
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10.1007/s00500-010-0560-8 doi (DE-627)SPR00647845X (SPR)s00500-010-0560-8-e DE-627 ger DE-627 rakwb eng Lai, Chien-Yo verfasserin aut Entropy-type classification maximum likelihood algorithms for mixture models 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. Classification maximum likelihood (CML) (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Fuzzy CML (dpeaa)DE-He213 Entropy (dpeaa)DE-He213 Entropy-type CML (dpeaa)DE-He213 Parameter-free (dpeaa)DE-He213 Yang, Miin-Shen verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 2 vom: 19. Feb., Seite 373-381 (DE-627)SPR006469531 nnns volume:15 year:2010 number:2 day:19 month:02 pages:373-381 https://dx.doi.org/10.1007/s00500-010-0560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 2 19 02 373-381 |
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Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. |
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Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. |
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Abstract Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods. |
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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">SPR00647845X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2010 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-010-0560-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR00647845X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-010-0560-8-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">Lai, Chien-Yo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Entropy-type classification maximum likelihood algorithms for mixture models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</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 Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification maximum likelihood (CML)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy clustering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy CML</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Entropy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Entropy-type CML</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parameter-free</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Miin-Shen</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">15(2010), 2 vom: 19. Feb., Seite 373-381</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:15</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:2</subfield><subfield code="g">day:19</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:373-381</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-010-0560-8</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">15</subfield><subfield code="j">2010</subfield><subfield code="e">2</subfield><subfield code="b">19</subfield><subfield code="c">02</subfield><subfield code="h">373-381</subfield></datafield></record></collection>
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