Knowledge Discovery from Legal Databases: Law and Philosophy Library, cartea 69
Autor Andrew Stranieri, John Zeleznikowen Limba Engleză Hardback – iun 2005
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Specificații
ISBN-13: 9781402030369
ISBN-10: 1402030363
Pagini: 312
Ilustrații: XII, 298 p.
Dimensiuni: 156 x 232 x 22 mm
Greutate: 0.61 kg
Ediția:2005
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Law and Philosophy Library
Locul publicării:Dordrecht, Netherlands
ISBN-10: 1402030363
Pagini: 312
Ilustrații: XII, 298 p.
Dimensiuni: 156 x 232 x 22 mm
Greutate: 0.61 kg
Ediția:2005
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Law and Philosophy Library
Locul publicării:Dordrecht, Netherlands
Public țintă
ResearchCuprins
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.
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.