Search (24 results, page 1 of 2)

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  • × theme_ss:"Data Mining"
  1. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.05
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  2. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02
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    Date
    2. 4.2000 18:01:22
  3. Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002) 0.01
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  4. Tiefschürfen in Datenbanken (2002) 0.01
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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
  5. Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002) 0.01
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  6. Mandl, T.: Text mining und data minig (2013) 0.01
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    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  7. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01
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    Date
    22.11.1998 18:57:22
  8. 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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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  9. Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000) 0.01
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    Abstract
    In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin
  10. Baumgartner, R.: Methoden und Werkzeuge zur Webdatenextraktion (2006) 0.01
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  11. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.01
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  12. Gaizauskas, R.; Wilks, Y.: Information extraction : beyond document retrieval (1998) 0.01
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  13. Pons-Porrata, A.; Berlanga-Llavori, R.; Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques (2007) 0.01
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  14. Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012) 0.01
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  15. Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018) 0.01
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  16. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01
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    Date
    22. 3.2013 19:43:01
  17. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  18. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01
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    Date
    7. 3.2019 16:32:22
  19. Keim, D.A.: Datenvisualisierung und Data Mining (2004) 0.01
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    Source
    Grundlagen der praktischen Information und Dokumentation. 5., völlig neu gefaßte Ausgabe. 2 Bde. Hrsg. von R. Kuhlen, Th. Seeger u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried. Bd.1: Handbuch zur Einführung in die Informationswissenschaft und -praxis
  20. Liu, Y.; Zhang, M.; Cen, R.; Ru, L.; Ma, S.: Data cleansing for Web information retrieval using query independent features (2007) 0.01
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