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  • × theme_ss:"Data Mining"
  1. Bath, P.A.: Data mining in health and medical information (2003) 0.00 
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    Date
    23.10.2005 18:29:03
  2. 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
  3. 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
  4. 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
  5. 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
  6. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.00 
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    Date
    29. 2.2008 17:14:09
  7. 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
  8. Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014) 0.00 
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    Date
    29. 1.2014 16:46:40
  9. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.00 
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    Date
    22. 3.2008 14:46:06
  10. 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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    Source
    Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase
  11. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.00 
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    Date
    25. 6.2013 19:05:29
  12. Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016) 0.00 
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    Date
    21. 7.2016 19:29:19
  13. Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017) 0.00 
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    Date
    16.11.2017 14:00:29
  14. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00 
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    Date
    22. 3.2013 19:43:01
  15. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  16. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.00 
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    Date
    7. 3.2019 16:32:22
  17. 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
  18. 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.
  19. Information visualization in data mining and knowledge discovery (2002) 0.00 
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    Date
    23. 3.2008 19:10:22

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