Search (55 results, page 1 of 3)

  • × theme_ss:"Data Mining"
  1. Mandl, T.: Text mining und data minig (2013) 0.04 
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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
  2. Tiefschürfen in Datenbanken (2002) 0.04 
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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
  3. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.03 
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  4. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.03 
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    Date
    22. 3.2013 19:43:01
  5. Baumgartner, R.: Methoden und Werkzeuge zur Webdatenextraktion (2006) 0.03 
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    Source
    Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer
  6. Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000) 0.02 
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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
  7. Relational data mining (2001) 0.02 
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    Abstract
    As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The ferst part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programmeng; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
    Editor
    Dzeroski, S. u. N. Lavrac
  8. Keim, D.A.: Datenvisualisierung und Data Mining (2004) 0.02 
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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
  9. Mining text data (2012) 0.02 
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    Editor
    Aggarwal, C.C. u. C.X. Zhai
    Footnote
    Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtI.3uq2.bW89MQ%5f%5fCXccFOL0.
  10. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.02 
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    Date
    22. 3.2008 14:46:06
  11. Knowledge management in fuzzy databases (2000) 0.02 
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    Editor
    Pons, O.
  12. Kipcic, O.; Cramer, C.: Wie Zeitungsinhalte Forschung und Entwicklung befördern (2017) 0.02 
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  13. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.02 
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    Abstract
    The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
  14. Mattison, R.: Data warehousing and data mining for telecommunications (1997) 0.01 
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  15. Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010) 0.01 
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  16. Cios, K.J.; Pedrycz, W.; Swiniarksi, R.: Data mining methods for knowledge discovery (1998) 0.01 
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  17. Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002) 0.01 
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  18. Data mining, data warehousing and client/server databases : Proceedings of the 8th International Hong Kong Computer Society Database Workshop (Industrial Stream) (1997) 0.01 
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    Editor
    Siu, B., P.K.M. Kwan u. B. Lam
  19. Data Mining im praktischen Einsatz : Verfahren und Anwendungsfälle für Marketing, Vertrieb, Controlling und Kundenunterstützung (2000) 0.01 
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    Editor
    Alpar, P. u. I. Niedereichholz
  20. Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002) 0.01 
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