Search (34 results, page 1 of 2)

  • × theme_ss:"Data Mining"
  1. Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008) 0.04 
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    Content
    "Vor Google gibt es kein Entrinnen. Nun macht sich die größte Internetsuchmaschine daran, auch gefährliche Grippewellen in den USA vorauszusagen - und das schneller als die US-Gesundheitsbehörde. In den Regionen, in denen die Influenza grassiert, häufen sich erfahrungsgemäß auch die Online-Anfragen im Internet speziell zu diesem Thema. "Wir haben einen engen Zusammenhang feststellen können zwischen Personen, die nach themenbezogenen Informationen suchen, und Personen, die tatsächlich an der Grippe erkrankt sind", schreibt Google. Ein Webtool namens "Google Flu Trends" errechnet aus den Anfragen die Ausbreitung von Grippeviren. Auch wenn nicht jeder Nutzer erkrankt sei, spiegele die Zahl der Anfragen doch ziemlich genau die Entwicklung einer Grippewelle wider. Das belege ein Vergleich mit den Daten der US-Seuchenkontrollbehörde CDC, die in den meisten Fällen nahezu identisch seien. Die Internet-Suchmaschine könne anders als die Gesundheitsbehörde täglich auf aktuelle Daten zurückgreifen. Dadurch sei Google in der Lage, die Grippesaison ein bis zwei Wochen früher vorherzusagen. Und Zeit bedeutet Leben, wie Lyn Finelli sagt, Leiter der Abteilung Influenza der USSeuchenkontrollbehörde: "Je früher wir gewarnt werden, desto früher können wir handeln. Dies kann die Anzahl der Erkrankten erheblich minimieren." "Google Flu Trends" ist das erste Projekt, das Datenbanken einer Suchmaschine nutzt, um einen auftretenden Grippevirus zu lokalisieren - zurzeit nur in den USA, aber weltweite Prognosen wären ein folgerichtiger nächster Schritt. Philip M. Polgreen von der Universität von Iowa verspricht sich noch viel mehr: "Theoretisch können wir diese Flut an Informationen dazu nutzen, auch den Verlauf anderer Krankheiten besser zu studieren." Um das Grippe-Ausbreitungsmodell zu erstellen, hat Google mehrere hundert Milliarden Suchanfragen aus den vergangenen Jahren analysiert. Datenschützer haben den Internetgiganten bereits mehrfach als "datenschutzfeindlich" eingestuft. Die Anwender wüssten weder, was mit den gesammelten Daten passiere, noch wie lange gespeicherte Informationen verfügbar seien. Google versichert jedoch, dass "Flu Trends" die Privatsphäre wahre. Das Tool könne niemals dazu genutzt werden, einzelne Nutzer zu identifizieren, da wir bei der Erstellung der Statistiken lediglich anonyme Datenmaterialien nutzen. Die Muster, die wir in den Daten analysieren, ergeben erst in einem größeren Kontext Sinn." An einer echten Virus-Grippe - nicht zu verwechseln mit einer Erkältung - erkranken weltweit mehrere Millionen Menschen, mehr als 500 000 sterben daran."
    Date
    3. 5.1997 8:44:22
  2. 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
  3. 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
  4. Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001) 0.01 
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    Date
    29. 3.2002 17:31:17
  5. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01 
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    Date
    2. 4.2000 18:01:22
  6. Witten, I.H.; Frank, E.: Data Mining : Praktische Werkzeuge und Techniken für das maschinelle Lernen (2000) 0.01 
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    Date
    27. 1.1996 10:29:55
  7. Keim, D.A.: Data Mining mit bloßem Auge (2002) 0.01 
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    Date
    31.12.1996 19:29:41
  8. Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002) 0.01 
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    Date
    31.12.1996 19:29:41
  9. Wrobel, S.: Lern- und Entdeckungsverfahren (2002) 0.01 
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    Date
    31.12.1996 19:29:41
  10. KDD : techniques and applications (1998) 0.01 
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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
  11. Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  12. Cardie, C.: Empirical methods in information extraction (1997) 0.00 
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    Date
    6. 3.1999 13:50:29
  13. Tiefschürfen in Datenbanken (2002) 0.00 
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    Date
    31.12.1996 19:29:41
  14. Bath, P.A.: Data mining in health and medical information (2003) 0.00 
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    Date
    23.10.2005 18:29:03
  15. 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
  16. 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
  17. 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
  18. 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
  19. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.00 
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    Date
    29. 2.2008 17:14:09
  20. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00 
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    Date
    1. 6.2010 9:29:57

Authors

Years

Languages

  • e 20
  • d 14

Types

Themes

Subjects

Classifications