Search (50 results, page 3 of 3)

  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  • × year_i:[2000 TO 2010}
  1. Relational data mining (2001) 0.00 
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    Theme
    Information Resources Management
  2. Srinivasan, P.: Text mining : generating hypotheses from MEDLINE (2004) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.5, S.396-413
  3. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.00 
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    Source
    Information processing and management. 40(2004) no.2, S.239-255
  4. Gluck , M.: Multimedia exploratory data analysis for geospatial data mining : the case for augmented seriation (2001) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.8, S.686-696
  5. Pons-Porrata, A.; Berlanga-Llavori, R.; Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques (2007) 0.00 
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    Source
    Information processing and management. 43(2007) no.3, S.752-768
  6. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.00 
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    Source
    Knowledge organization, information systems and other essays: Professor A. Neelameghan Festschrift. Ed. by K.S. Raghavan and K.N. Prasad
  7. Thelwall, M.; Wilkinson, D.; Uppal, S.: Data mining emotion in social network communication : gender differences in MySpace (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.190-199
  8. Dang, X.H.; Ong. K.-L.: Knowledge discovery in data streams (2009) 0.00 
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    Source
    Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates
  9. Kantardzic, M.: Data mining : concepts, models, methods, and algorithms (2003) 0.00 
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    Abstract
    This book offers a comprehensive introduction to the exploding field of data mining. We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. "Data Mining: Concepts, Models, Methods, and Algorithms" discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.
  10. Schwartz, F.; Fang, Y.C.: Citation data analysis on hydrogeology (2007) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.4, S.518-525

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