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Main Data
Author: Andrew Stranieri, John Zeleznikow
Title: Knowledge Discovery from Legal Databases
Publisher: Springer-Verlag
ISBN/ISSN: 9781402030376
Edition: 1
Price: CHF 202.30
Publication date: 01/01/2006
Content
Category: Kunst/Grafik/Fotografie
Language: English
Technical Data
Pages: 298
Kopierschutz: DRM
Geräte: PC/MAC/eReader/Tablet
Formate: PDF
Table of contents

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.

Table of contents
CONTENTS6
ACKNOWLEDGEMENTS7
PREFACE9
CHAPTER 1 INTRODUCTION13
1. KNOWLEDGE DISCOVERY FROM DATABASES IN LAW14
2. CONCEPTUALALISING DATA20
3. PHASES IN THE KNOWLEDGE DISCOVERY FROM DATABASE PROCESS22
4. DIFFERENCES BETWEEN LEGAL AND OTHER DATA23
5. CHAPTER SUMMARY24
CHAPTER 2 LEGAL ISSUES IN THE DATA SELECTION PHASE27
1. OPEN TEXTURE, DISCRETION AND KDD27
2. STARE DECISIS31
3. CIVIL AND COMMON LAW C34
4. SELECTING A TASK SUITABLE FOR KDD: THE IMPORTANCE OF OPEN TEXTURE37
5. SAMPLE ASSESSMENT OF THE DEGREE OF OPEN TEXTURE42
6. SELECTING DATASET RECORDS44
7. CHAPTER SUMMARY56
CHAPTER 3 LEGAL ISSUES IN THE DATA PRE-PROCESSING PHASE59
1. MISSING DATA59
2. INCONSISTENT DATA61
3. CHAPTER SUMMARY70
CHAPTER 4 LEGAL ISSUES IN THE DATA TRANSFORMATION PHASE71
1. AGGREGATING VALUES72
2. NORMALISING73
3. FEATURE OR EXAMPLE REDUCTION74
4. THE USE OF ARGUMENTATION FOR RESTRUCTURING75
5. CHAPTER SUMMARY93
CHAPTER 5 DATA MINING WITH RULE INDUCTION95
1. RULE INDUCTION WITH ID397
2. USES OF RULE INDUCTION IN LAW107
3. CHAPTER SUMMARY109
CHAPTER 6 UNCERTAIN AND STATISTICAL DATA MINING111
1. DATA MINING USING ASSOCIATION RULES111
2. FUZZY REASONING123
3. BAYESIAN CLASSIFICATION127
4. CERTAINTY FACTORS133
5. NEAREST NEIGHBOUR APPROACHES134
6. EVOLUTIONARY COMPUTING AND GENETIC ALGORITHMS135
7. KERNEL MACHINES136
8. SUPPORT VECTOR MACHINES137
9. CHAPTER SUMMARY139
CHAPTER 7 DATA MINING USING NEURAL NETWORKS141
1. FEED FORWARD NETWORKS141
2. NEURAL NETWORKS IN LAW152
3. CHAPTER SUMMARY157
CHAPTER 8 INFORMATION RETRIEVAL AND TEXT MINING159
1. INFORMATION RETRIEVAL BASICS159
2. INFORMATION RETRIEVAL IN LAW166
3. TEXT MINING IN LAW170
4. WEB MINING179
5. CHAPTER SUMMARY180
CHAPTER 9 EVALUATION, DEPLOYMENT AND RELATED ISSUES183
1. GENERALISATION183
2. BOOSTING AND BAGGING191
3. FRAMEWORKS FOR EVALUATING LEGAL KNOWLEDGE BASED SYSTEMS192
4. EXPLANATION210
5. SELECTING SUITABLE FIELDS OF LAW214
6. LEGAL ONTOLOGIES216
7. CHAPTER SUMMARY221
CHAPTER 10 CONCLUSION223
1. THE VALIDITY OF USING KDD IN LEGAL DOMAINS223
2. KDD AND REASONING WITH CASES225
3. WHAT LEGAL DOMAINS ARE AMENABLE TO THE USE OF KDD226
4. PREPARING LEGAL DATA FOR USE IN THE KDD PROCESS228
5. TECHNIQUES FOR PERFORMING KDD IN LEGAL DATABASES229
6. UNDERSTANDING AND JUSTIFYING THE RESULTS OF THE KDD PROCESS232
7. HOW KNOWLEDGE DISCOVERY IN LAW CAN ENHANCE ACCESS TO JUSTICE233
8. CURRENT AND FUTURE RESEARCH IN KNOWLEDGE DISCOVERY IN LAW235
11 BIBLIOGRAPHY239
12 GLOSSARY267
INDEX295