Advanced Modelling Paradigms in Data Mining

  • Dawn E. Holmes
  • Jeffrey Tweedale
  • Lakhmi C. Jain
Part of the Intelligent Systems Reference Library book series (ISRL, volume 24)


As discussed in the previous volume, the term Data Mining grew from the relentless growth of techniques used to interrogation masses of data. As a myriad of databases emanated from disparate industries, enterprise management insisted their information officers develop methodology to exploit the knowledge held in their repositories. Industry has invested heavily to gain knowledge they can exploit to gain a market advantage. This includes extracting hidden data, trends or pattern from what was traditionally considered noise. For instance most corporations track sales, stock, pay role and other operational information. Acquiring and maintaing these repositories relies on mainstream techniques, technology and methodologies. In this book we discuss a number of founding techniques and expand into intelligent paradigms.


Support Vector Machine Data Mining Learn Decision Tree Ellipsoidal Uncertainty Market Advantage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dawn E. Holmes
    • 1
  • Jeffrey Tweedale
    • 2
  • Lakhmi C. Jain
    • 2
  1. 1.Department of Statistics and Applied ProbabilityUniversity of California Santa BarbaraSanta BarbaraUSA
  2. 2.School of Electrical and Information EngineeringUniversity of South AustraliaAdelaideAustralia

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