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Knowledge Discovery from Legal Databases: Law and Philosophy Library, cartea 69

Autor Andrew Stranieri, John Zeleznikow
en Limba Engleză Paperback – 28 oct 2010
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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Specificații

ISBN-13: 9789048167715
ISBN-10: 904816771X
Pagini: 312
Ilustrații: XII, 298 p.
Dimensiuni: 160 x 240 x 17 mm
Greutate: 0.5 kg
Ediția:Softcover reprint of hardcover 1st edition 2005
Editura: Springer
Colecția Law and Philosophy Library
Seria Law and Philosophy Library

Locul publicării:Dordrecht, Netherlands

Public țintă

Research

Cuprins

Legal Issues in the Data Selection Phase.- Legal Issues in the Data Pre-Processing Phase.- Legal Issues in the Data Transformation Phase.- Data Mining with Rule Induction.- Uncertain and Statistical Data Mining.- Data Mining Using Neural Networks.- Information Retrieval and Text Mining.- Evaluation, Deployment and Related Issues.- Conclusion.

Textul de pe ultima copertă

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.