Search (57 results, page 1 of 3)

  • × theme_ss:"Data Mining"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003) 0.01 
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    Date
    22. 4.2003 11:45:36
    Pages
    S.123-133
  2. Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008) 0.01 
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    Date
    3. 5.1997 8:44:22
    Source
    Kölner Stadt-Anzeiger. Nr.267 vom 14.11.2008, S.4
  3. Lischka, K.: Spurensuche im Datenwust : Data-Mining-Software fahndet nach kriminellen Mitarbeitern, guten Kunden - und bald vielleicht auch nach Terroristen (2002) 0.01 
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    Content
    "Ob man als Terrorist einen Anschlag gegen die Vereinigten Staaten plant, als Kassierer Scheine aus der Kasse unterschlägt oder für bestimmte Produkte besonders gerne Geld ausgibt - einen Unterschied macht Data-Mining-Software da nicht. Solche Programme analysieren riesige Daten- mengen und fällen statistische Urteile. Mit diesen Methoden wollen nun die For- scher des "Information Awaren in den Vereinigten Staaten Spuren von Terroristen in den Datenbanken von Behörden und privaten Unternehmen wie Kreditkartenfirmen finden. 200 Millionen Dollar umfasst der Jahresetat für die verschiedenen Forschungsprojekte. Dass solche Software in der Praxis funktioniert, zeigen die steigenden Umsätze der Anbieter so genannter Customer-Relationship-Management-Software. Im vergangenen Jahr ist das Potenzial für analytische CRM-Anwendungen laut dem Marktforschungsinstitut IDC weltweit um 22 Prozent gewachsen, bis zum Jahr 2006 soll es in Deutschland mit einem jährlichen Plus von 14,1 Prozent so weitergehen. Und das trotz schwacher Konjunktur - oder gerade deswegen. Denn ähnlich wie Data-Mining der USRegierung helfen soll, Terroristen zu finden, entscheiden CRM-Programme heute, welche Kunden für eine Firma profitabel sind. Und welche es künftig sein werden, wie Manuela Schnaubelt, Sprecherin des CRM-Anbieters SAP, beschreibt: "Die Kundenbewertung ist ein zentraler Bestandteil des analytischen CRM. Sie ermöglicht es Unternehmen, sich auf die für sie wichtigen und richtigen Kunden zu fokussieren. Darüber hinaus können Firmen mit speziellen Scoring- Verfahren ermitteln, welche Kunden langfristig in welchem Maße zum Unternehmenserfolg beitragen." Die Folgen der Bewertungen sind für die Betroffenen nicht immer positiv: Attraktive Kunden profitieren von individuellen Sonderangeboten und besonderer Zuwendung. Andere hängen vielleicht so lauge in der Warteschleife des Telefonservice, bis die profitableren Kunden abgearbeitet sind. So könnte eine praktische Umsetzung dessen aussehen, was SAP-Spreche-rin Schnaubelt abstrakt beschreibt: "In vielen Unternehmen wird Kundenbewertung mit der klassischen ABC-Analyse durchgeführt, bei der Kunden anhand von Daten wie dem Umsatz kategorisiert werden. A-Kunden als besonders wichtige Kunden werden anders betreut als C-Kunden." Noch näher am geplanten Einsatz von Data-Mining zur Terroristenjagd ist eine Anwendung, die heute viele Firmen erfolgreich nutzen: Sie spüren betrügende Mitarbeiter auf. Werner Sülzer vom großen CRM-Anbieter NCR Teradata beschreibt die Möglichkeiten so: "Heute hinterlässt praktisch jeder Täter - ob Mitarbeiter, Kunde oder Lieferant - Datenspuren bei seinen wirtschaftskriminellen Handlungen. Es muss vorrangig darum gehen, einzelne Spuren zu Handlungsmustern und Täterprofilen zu verdichten. Das gelingt mittels zentraler Datenlager und hoch entwickelter Such- und Analyseinstrumente." Von konkreten Erfolgen sprich: Entlas-sungen krimineller Mitarbeiter-nach Einsatz solcher Programme erzählen Unternehmen nicht gerne. Matthias Wilke von der "Beratungsstelle für Technologiefolgen und Qualifizierung" (BTQ) der Gewerkschaft Verdi weiß von einem Fall 'aus der Schweiz. Dort setzt die Handelskette "Pick Pay" das Programm "Lord Lose Prevention" ein. Zwei Monate nach Einfüh-rung seien Unterschlagungen im Wert von etwa 200 000 Franken ermittelt worden. Das kostete mehr als 50 verdächtige Kassiererinnen und Kassierer den Job.
    Source
    Frankfurter Rundschau. Nr.275 vom 26.11.2002, S.11
  4. Wrobel, S.: Lern- und Entdeckungsverfahren (2002) 0.00 
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    Source
    Spektrum der Wissenschaft. 2002, H.11, S.85-87
  5. Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001) 0.00 
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    Source
    Knowledge-based systems. 14(2001) nos.1/2, S.37-53
  6. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.00 
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    Source
    Applied artificial intelligence. 16(2002) no.4, S.283-292
  7. Keim, D.A.: Data Mining mit bloßem Auge (2002) 0.00 
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    Source
    Spektrum der Wissenschaft. 2002, H.11, S.88-91
  8. Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002) 0.00 
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    Spektrum der Wissenschaft. 2002, H.11, S.80-81
  9. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.00 
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    Source
    Journal of information science. 30(2005) no.6, S.550-
  10. Tiefschürfen in Datenbanken (2002) 0.00 
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    Content
    Enthält die Beiträge: Kruse, R., C. Borgelt: Suche im Datendschungel - Borgelt, C. u. R. Kruse: Unsicheres Wissen nutzen - Wrobel, S.: Lern- und Entdeckungsverfahren - Keim, D.A.: Data Mining mit bloßem Auge
    Source
    Spektrum der Wissenschaft. 2002, H.11, S.80-91
  11. Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002) 0.00 
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    Source
    Spektrum der Wissenschaft. 2002, H.11, S.82-84
  12. Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008) 0.00 
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    Pages
    S.181-186
    Source
    Kompatibilität, Medien und Ethik in der Wissensorganisation - Compatibility, Media and Ethics in Knowledge Organization: Proceedings der 10. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation Wien, 3.-5. Juli 2006 - Proceedings of the 10th Conference of the German Section of the International Society of Knowledge Organization Vienna, 3-5 July 2006. Ed.: H.P. Ohly, S. Netscher u. K. Mitgutsch
  13. Loh, S.; Oliveira, J.P.M. de; Gastal, F.L.: Knowledge discovery in textual documentation : qualitative and quantitative analyses (2001) 0.00 
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    Source
    Journal of documentation. 57(2001) no.5, S.577-590
  14. Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1805-1819
  15. Thelwall, M.; Wilkinson, D.; Uppal, S.: Data mining emotion in social network communication : gender differences in MySpace (2009) 0.00 
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    Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.190-199
  16. Sperlich, T.: ¬Die Zukunft hat schon begonnen : Visualisierungssoftware in der praktischen Anwendung (2000) 0.00 
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    Pages
    S.342-375
  17. Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.00 
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    c't. 2003, H.19, S.192-197
  18. Bath, P.A.: Data mining in health and medical information (2003) 0.00 
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    Source
    Annual review of information science and technology. 38(2004), S.331-370
  19. Nicholson, S.: Bibliomining for automated collection development in a digital library setting : using data mining to discover Web-based scholarly research works (2003) 0.00 
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  20. Liu, Y.; Zhang, M.; Cen, R.; Ru, L.; Ma, S.: Data cleansing for Web information retrieval using query independent features (2007) 0.00 
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