Search (155 results, page 8 of 8)

  • × theme_ss:"Data Mining"
  1. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.00 
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  2. Kipcic, O.; Cramer, C.: Wie Zeitungsinhalte Forschung und Entwicklung befördern (2017) 0.00 
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  3. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.00 
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  4. Wiegmann, S.: Hättest du die Titanic überlebt? : Eine kurze Einführung in das Data Mining mit freier Software (2023) 0.00 
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  5. Heyer, G.; Läuter, M.; Quasthoff, U.; Wolff, C.: Texttechnologische Anwendungen am Beispiel Text Mining (2000) 0.00 
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  6. Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008) 0.00 
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  7. Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.00 
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  8. Carter, D.; Sholler, D.: Data science on the ground : hype, criticism, and everyday work (2016) 0.00 
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  9. Drees, B.: Text und data mining : Herausforderungen und Möglichkeiten für Bibliotheken (2016) 0.00 
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  10. Loonus, Y.: Einsatzbereiche der KI und ihre Relevanz für Information Professionals (2017) 0.00 
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  11. Keim, D.A.: Datenvisualisierung und Data Mining (2004) 0.00 
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  12. Survey of text mining : clustering, classification, and retrieval (2004) 0.00 
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    Abstract
    Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
  13. Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007) 0.00 
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  14. Klein, H.: Web Content Mining (2004) 0.00 
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  15. Seidenfaden, U.: Schürfen in Datenbergen : Data-Mining soll möglichst viel Information zu Tage fördern (2001) 0.00 
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