Search (173 results, page 9 of 9)

  • × theme_ss:"Data Mining"
  1. Carter, D.; Sholler, D.: Data science on the ground : hype, criticism, and everyday work (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.10, S.2309-2319
    Type
    a
  2. 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.
    Pages
    XVII, 244 S
    Type
    s
  3. Advances in knowledge discovery and data mining (1996) 0.00 
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    Footnote
    Rez. in: JASIS 49(1998) no.4, S.386-387 (F. Exner)
    Pages
    625 S
    Type
    s
  4. Data mining, data warehousing and client/server databases : Proceedings of the 8th International Hong Kong Computer Society Database Workshop (Academic Stream) (1997) 0.00 
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    Pages
    345 S
    Type
    s
  5. Intelligent information processing and web mining : Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane, Poland, June 2-5, 2003 (2003) 0.00 
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    Pages
    XIV, 579 S
    Type
    s
  6. Mohr, J.W.; Bogdanov, P.: Topic models : what they are and why they matter (2013) 0.00 
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    Abstract
    We provide a brief, non-technical introduction to the text mining methodology known as "topic modeling." We summarize the theory and background of the method and discuss what kinds of things are found by topic models. Using a text corpus comprised of the eight articles from the special issue of Poetics on the subject of topic models, we run a topic model on these articles, both as a way to introduce the methodology and also to help summarize some of the ways in which social and cultural scientists are using topic models. We review some of the critiques and debates over the use of the method and finally, we link these developments back to some of the original innovations in the field of content analysis that were pioneered by Harold D. Lasswell and colleagues during and just after World War II.
    Type
    a
  7. Decker, B.: Data Mining in Öffentlichen Bibliotheken (2000) 0.00 
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    Pages
    III,45,V S
  8. Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016) 0.00 
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    Abstract
    Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effectively. We'll walk through a series of simple examples to illustrate what t-SNE diagrams can and cannot show. The t-SNE technique really is useful-but only if you know how to interpret it.
    Type
    a
  9. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.00 
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    Abstract
    This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.
    Type
    a
  10. Data mining : Theoretische Aspekte und Anwendungen (1998) 0.00 
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    Pages
    XII,363 S
    Type
    s
  11. Ester, M.; Sander, J.: Knowledge discovery in databases : Techniken und Anwendungen (2000) 0.00 
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    Pages
    VIII, 281 S
  12. Loonus, Y.: Einsatzbereiche der KI und ihre Relevanz für Information Professionals (2017) 0.00 
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    a
  13. Witschel, H.F.: Text, Wörter, Morpheme : Möglichkeiten einer automatischen Terminologie-Extraktion (2004) 0.00 
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    Pages
    141 S

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