Knowledge Discovery from Legal Databases

Overview

Knowledge Discovery from Legal Databases is the first text to describe data mining techniques as they apply to law. Law students, legal academics and applied information technology specialists are guided thorough all phases of the knowledge discovery from databases process with clear explanations of numerous data mining algorithms including rule induction, neural networks and association rules. Throughout the text, assumptions that make data mining in law quite different to mining other data are made explicit. ...

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Paperback (Softcover reprint of hardcover 1st ed. 2005)
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Overview

Knowledge Discovery from Legal Databases is the first text to describe data mining techniques as they apply to law. Law students, legal academics and applied information technology specialists are guided thorough all phases of the knowledge discovery from databases process with clear explanations of numerous data mining algorithms including rule induction, neural networks and association rules. Throughout the text, assumptions that make data mining in law quite different to mining other data are made explicit. Issues such as the selection of commonplace cases, the use of discretion as a form of open texture, transformation using argumentation concepts and evaluation and deployment approaches are discussed at length.

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Product Details

  • ISBN-13: 9789048167715
  • Publisher: Springer Netherlands
  • Publication date: 1/11/2011
  • Series: Law and Philosophy Library Series , #69
  • Edition description: Softcover reprint of hardcover 1st ed. 2005
  • Edition number: 1
  • Pages: 294
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.65 (d)

Table of Contents

Acknowledgements.- Preface.- 1. Introduction.- 2. Legal Issues In The Data Selection Phase.- 3. Legal Issues In The Data Pre-Processing Phase.- 4. Legal Issues In The Data Transformation Phase.- 5. Data Mining With Rule Induction.- 6. Uncertain And Statistical Data Mining.- 7. Data Mining Using Neural Networks.- 8. Information Retrieval And Text Mining.- 9. Evaluation, Deployment And Related Issues.- 10. Conclusion.- 11. Bibliography.- 12. Glossary.- 13. Index

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