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  • × theme_ss:"Data Mining"
  1. Sperlich, T.: ¬Die Zukunft hat schon begonnen : Visualisierungssoftware in der praktischen Anwendung (2000) 0.01 
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    Content
    1. Unsichtbares sichtbar machen - 2. Mit 3D-Darsteltungen besser verkaufen - 3. Mixed Realities - 4. Informationstechnik hilft heilen - 5. Informationen finden - Komplexes verstehen - 6. Informationslandschaften - Karten - 7. Arbeiten und Wohnen in der Info-Zukunft - 8. Neues Lernen in der Info-Welt - 9. Computerspiele alsTechnologie-Avantgarde - 10. Multimediale Kunst
  2. Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006) 0.01 
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    Abstract
    Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das Forschungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Das erste deutsche Lehrbuch zu einer bahnbrechenden Technologie: Text Mining: Wissensrohstoff Text Konzepte, Algorithmen, Ergebnisse Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das For-schungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Was fällt Ihnen bei dem Wort "Stich" ein? Die einen denken an Tennis, die anderen an Skat. Die verschiedenen Zusammenhänge können durch Text Mining automatisch ermittelt und in Form von Wortnetzen dargestellt werden. Welche Begriffe stehen am häufigsten links und rechts vom Wort "Festplatte"? Welche Wortformen und Eigennamen treten seit 2001 neu in der deutschen Sprache auf? Text Mining beantwortet diese und viele weitere Fragen. Tauchen Sie mit diesem Lehrbuch ein in eine neue, faszinierende Wissenschaftsdisziplin und entdecken Sie neue, bisher unbekannte Zusammenhänge und Sichtweisen. Sehen Sie, wie aus dem Wissensrohstoff Text Wissen wird! Dieses Lehrbuch richtet sich sowohl an Studierende als auch an Praktiker mit einem fachlichen Schwerpunkt in der Informatik, Wirtschaftsinformatik und/oder Linguistik, die sich über die Grundlagen, Verfahren und Anwendungen des Text Mining informieren möchten und Anregungen für die Implementierung eigener Anwendungen suchen. Es basiert auf Arbeiten, die während der letzten Jahre an der Abteilung Automatische Sprachverarbeitung am Institut für Informatik der Universität Leipzig unter Leitung von Prof. Dr. Heyer entstanden sind. Eine Fülle praktischer Beispiele von Text Mining-Konzepten und -Algorithmen verhelfen dem Leser zu einem umfassenden, aber auch detaillierten Verständnis der Grundlagen und Anwendungen des Text Mining. Folgende Themen werden behandelt: Wissen und Text Grundlagen der Bedeutungsanalyse Textdatenbanken Sprachstatistik Clustering Musteranalyse Hybride Verfahren Beispielanwendungen Anhänge: Statistik und linguistische Grundlagen 360 Seiten, 54 Abb., 58 Tabellen und 95 Glossarbegriffe Mit kostenlosen e-learning-Kurs "Schnelleinstieg: Sprachstatistik" Zusätzlich zum Buch gibt es in Kürze einen Online-Zertifikats-Kurs mit Mentor- und Tutorunterstützung.
  3. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01 
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    Date
    5. 4.1996 15:29:15
    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  4. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01 
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    Date
    5. 4.1996 15:29:15
    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  5. Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001) 0.00 
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    Date
    29. 3.2002 17:31:17
  6. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.00 
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    Date
    2. 4.2000 18:01:22
  7. Witten, I.H.; Frank, E.: Data Mining : Praktische Werkzeuge und Techniken für das maschinelle Lernen (2000) 0.00 
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    Date
    27. 1.1996 10:29:55
  8. Keim, D.A.: Data Mining mit bloßem Auge (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  9. Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  10. Wrobel, S.: Lern- und Entdeckungsverfahren (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  11. KDD : techniques and applications (1998) 0.00 
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  12. Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  13. Cardie, C.: Empirical methods in information extraction (1997) 0.00 
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    Date
    6. 3.1999 13:50:29
  14. Tiefschürfen in Datenbanken (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  15. Bath, P.A.: Data mining in health and medical information (2003) 0.00 
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    Date
    23.10.2005 18:29:03
  16. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.00 
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    Date
    22.11.1998 18:57:22
  17. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.00 
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    Date
    17. 7.2002 19:22:06
  18. Methodologies for knowledge discovery and data mining : Third Pacific-Asia Conference, PAKDD'99, Beijing, China, April 26-28, 1999, Proceedings (1999) 0.00 
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    Abstract
    The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization, causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies
  19. Wiegmann, S.: Hättest du die Titanic überlebt? : Eine kurze Einführung in das Data Mining mit freier Software (2023) 0.00 
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    Date
    28. 1.2022 11:05:29
  20. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.00 
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    Date
    29. 2.2008 17:14:09

Authors

Years

Languages

  • e 21
  • d 16

Types

Themes

Subjects

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