Knowledge Discovery from Legal Databases / by Andrew Stranieri, John Zeleznikow.
2005
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Author
Title
Knowledge Discovery from Legal Databases / by Andrew Stranieri, John Zeleznikow.
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Added Corporate Author
Edition
1st ed. 2005.
Imprint
Dordrecht : Springer Netherlands : Imprint: Springer, 2005.
Description
XII, 298 p. online resource.
Series
Law and philosophy library. 1572-4395 ; 69.
Formatted Contents Note
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.
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.
Summary
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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Alternate Title
SpringerLink electronic monographs.
Language
English
ISBN
9781402030376
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