Search (23 results, page 1 of 2)

  • × year_i:[2000 TO 2010}
  • × theme_ss:"Data Mining"
  1. Keim, D.A.: Data Mining mit bloßem Auge (2002) 0.01 
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    Date
    27.10.2002 14:12:56
  2. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.01 
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    Date
    22. 3.2008 14:46:06
  3. Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007) 0.01 
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    Date
    2.11.2007 12:58:52
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1805-1819
  4. 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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    Date
    19.10.2003 12:07:37
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.12, S.1081-1090
  5. Raan, A.F.J. van; Noyons, E.C.M.: Discovery of patterns of scientific and technological development and knowledge transfer (2002) 0.00 
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    Date
    1. 7.2005 12:10:16
    2. 7.2005 10:12:14
  6. Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1791-1792
  7. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804
  8. Kantardzic, M.: Data mining : concepts, models, methods, and algorithms (2003) 0.00 
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    Classification
    006.3/12 / dc22
    Date
    24. 3.2008 12:33:55
    DDC
    006.3/12 / dc22
  9. Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003) 0.00 
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    Date
    22. 4.2003 11:45:36
  10. Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008) 0.00 
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    Date
    3. 5.1997 8:44:22
  11. Benoit, G.: Data mining (2002) 0.00 
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    Date
    24.10.2005 12:38:26
  12. Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000) 0.00 
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    Date
    19.10.1996 9:59:12
  13. Haravu, L.J.; Neelameghan, A.: Text mining and data mining in knowledge organization and discovery : the making of knowledge-based products (2003) 0.00 
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    Date
    5. 6.2001 12:25:05
  14. Shi, X.; Yang, C.C.: Mining related queries from Web search engine query logs using an improved association rule mining model (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1871-1883
  15. Wang, F.L.; Yang, C.C.: Mining Web data for Chinese segmentation (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1820-1837
  16. Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007) 0.00 
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    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1838-1850
  17. Liu, Y.; Huang, X.; An, A.: Personalized recommendation with adaptive mixture of markov models (2007) 0.00 
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    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1851-1870
  18. 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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    Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1884-1898
  19. Lischka, K.: Spurensuche im Datenwust : Data-Mining-Software fahndet nach kriminellen Mitarbeitern, guten Kunden - und bald vielleicht auch nach Terroristen (2002) 0.00 
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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.
  20. Medien-Informationsmanagement : Archivarische, dokumentarische, betriebswirtschaftliche, rechtliche und Berufsbild-Aspekte ; [Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar und Folgetagung 2001 in Köln] (2003) 0.00 
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    Date
    11. 5.2008 19:49:22

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