A Unified Fuzzy Data Model : Representation and Processing
A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are pop...
Ausführliche Beschreibung
Autor*in: |
Gelbard, Roy [verfasserIn] Meged, Avichai [author] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2012 |
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Schlagwörter: |
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Umfang: |
Online-Ressource |
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Reproduktion: |
IGI Global InfoSci Journals Archive 2000 - 2012 |
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Übergeordnetes Werk: |
In: Journal of database management - Hershey, Pa : IGI Global, 2000, 23(2012), 1, Seite 78-102 |
Übergeordnetes Werk: |
volume:23 ; year:2012 ; number:1 ; pages:78-102 |
Links: |
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DOI / URN: |
10.4018/jdm.2012010104 |
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NLEJ244519056 |
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10.4018/jdm.2012010104 doi (DE-627)NLEJ244519056 (VZGNL)10.4018/jdm.2012010104 DE-627 ger DE-627 rakwb eng Gelbard, Roy verfasserin aut A Unified Fuzzy Data Model Representation and Processing 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however, standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets IGI Global InfoSci Journals Archive 2000 - 2012 Binary Databases Clustering Data Mining Data Representation Models Fuzzy Data Fuzzy Databases Meged, Avichai author aut In Journal of database management Hershey, Pa : IGI Global, 2000 23(2012), 1, Seite 78-102 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:23 year:2012 number:1 pages:78-102 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2012010104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2012010104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 23 2012 1 78-102 |
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10.4018/jdm.2012010104 doi (DE-627)NLEJ244519056 (VZGNL)10.4018/jdm.2012010104 DE-627 ger DE-627 rakwb eng Gelbard, Roy verfasserin aut A Unified Fuzzy Data Model Representation and Processing 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however, standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets IGI Global InfoSci Journals Archive 2000 - 2012 Binary Databases Clustering Data Mining Data Representation Models Fuzzy Data Fuzzy Databases Meged, Avichai author aut In Journal of database management Hershey, Pa : IGI Global, 2000 23(2012), 1, Seite 78-102 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:23 year:2012 number:1 pages:78-102 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2012010104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdm.2012010104&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 23 2012 1 78-102 |
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A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however, standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets |
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A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however, standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets |
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A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however, standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets |
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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">NLEJ244519056</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240202180314.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">150605s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/jdm.2012010104</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ244519056</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/jdm.2012010104</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">Gelbard, Roy</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Unified Fuzzy Data Model</subfield><subfield code="b">Representation and Processing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A novel fuzzy data representation model which enables data mining with standard tools is introduced. 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