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  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  • × type_ss:"m"
  1. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.01
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    Editor
    Gaul, W. u. G. Ritter
  2. Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006) 0.01
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  3. Information visualization in data mining and knowledge discovery (2002) 0.00
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    Date
    23. 3.2008 19:10:22
    Editor
    Fayyad, U. et al.
  4. Relational data mining (2001) 0.00
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    Editor
    Dzeroski, S. u. N. Lavrac
  5. Mining text data (2012) 0.00
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    Editor
    Aggarwal, C.C. u. C.X. Zhai
  6. Chakrabarti, S.: Mining the Web : discovering knowledge from hypertext data (2003) 0.00
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    Footnote
    Rez. in: JASIST 55(2004) no.3, S.275-276 (C. Chen): "This is a book about finding significant statistical patterns on the Web - in particular, patterns that are associated with hypertext documents, topics, hyperlinks, and queries. The term pattern in this book refers to dependencies among such items. On the one hand, the Web contains useful information an just about every topic under the sun. On the other hand, just like searching for a needle in a haystack, one would need powerful tools to locate useful information an the vast land of the Web. Soumen Chakrabarti's book focuses an a wide range of techniques for machine learning and data mining an the Web. The goal of the book is to provide both the technical Background and tools and tricks of the trade of Web content mining. Much of the technical content reflects the state of the art between 1995 and 2002. The targeted audience is researchers and innovative developers in this area, as well as newcomers who intend to enter this area. The book begins with an introduction chapter. The introduction chapter explains fundamental concepts such as crawling and indexing as well as clustering and classification. The remaining eight chapters are organized into three parts: i) infrastructure, ii) learning and iii) applications.