| Knowledge Discovery from Legal Databases 2005 Edition Contributor(s): Stranieri, Andrew (Author), Zeleznikow, John (Author) |
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ISBN: 1402030363 ISBN-13: 9781402030369 Publisher: Springer OUR PRICE: $208.99 Product Type: Hardcover - Other Formats Published: June 2005 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Social Science - Computers | Intelligence (ai) & Semantics - Law | Reference |
| Dewey: 340.028 |
| Series: Law and Philosophy Library |
| Physical Information: 0.75" H x 6.14" W x 9.21" (1.35 lbs) 298 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |