Search (21 results, page 1 of 2)

  • × theme_ss:"Data Mining"
  • × language_ss:"e"
  1. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.06
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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. Mattison, R.: Data warehousing and data mining for telecommunications (1997) 0.02
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  4. KDD : techniques and applications (1998) 0.02
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  5. Cios, K.J.; Pedrycz, W.; Swiniarksi, R.: Data mining methods for knowledge discovery (1998) 0.02
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  6. 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
  7. 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
  8. 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
  9. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.01
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  10. Relational data mining (2001) 0.01
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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.
  11. Gaizauskas, R.; Wilks, Y.: Information extraction : beyond document retrieval (1998) 0.01
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  12. Pons-Porrata, A.; Berlanga-Llavori, R.; Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques (2007) 0.01
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  13. 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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  14. Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018) 0.01
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  15. Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011) 0.01
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    Footnote
    Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtE.3uq2.bW89MQ%5f%5fCXPUFOH0.
  16. Mining text data (2012) 0.01
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    Footnote
    Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtI.3uq2.bW89MQ%5f%5fCXccFOL0.
  17. 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
  18. 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
  19. 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
  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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