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.
Author(s): Andrew Stranieri, John Zeleznikow,
Edition: 1
Year: 2005
Language: English
Pages: 294
1402030363......Page 1
CONTENTS......Page 6
Acknowledgements......Page 7
Preface......Page 9
Introduction......Page 13
LEGAL ISSUES IN THE DATA SELECTION PHASE......Page 26
LEGAL ISSUES IN THE DATA PRE-PROCESSING PHASE......Page 57
LEGAL ISSUES IN THE DATA TRANSFORMATION PHASE......Page 69
DATA MINING WITH RULE INDUCTION......Page 93
UNCERTAIN AND STATISTICAL DATA MINING......Page 109
DATA MINING USING NEURAL NETWORKS......Page 139
INFORMATION RETRIEVAL AND TEXT MINING......Page 157
EVALUATION, DEPLOYMENT AND RELATED ISSUES......Page 180
CONCLUSION......Page 220
BIBLIOGRAPHY......Page 236
A......Page 264
B......Page 265
C......Page 268
D......Page 271
E......Page 274
F......Page 275
H......Page 277
I......Page 278
K......Page 281
L......Page 282
N......Page 283
O......Page 284
P......Page 285
S......Page 287
T......Page 289
W......Page 290
B......Page 292
C......Page 293
D......Page 294
F......Page 296
J......Page 297
K......Page 298
N......Page 299
O......Page 300
S......Page 301
Z......Page 303
