E-Book, Englisch, Band 69, 307 Seiten
Reihe: Law and Philosophy Library
Stranieri / Zeleznikow Knowledge Discovery from Legal Databases
1. Auflage 2006
ISBN: 978-1-4020-3037-6
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 69, 307 Seiten
Reihe: Law and Philosophy Library
ISBN: 978-1-4020-3037-6
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Autoren/Hrsg.
Weitere Infos & Material
1;CONTENTS;6
2;ACKNOWLEDGEMENTS;7
3;PREFACE;9
4;CHAPTER 1 INTRODUCTION;13
4.1;1. KNOWLEDGE DISCOVERY FROM DATABASES IN LAW;14
4.2;2. CONCEPTUALALISING DATA;20
4.3;3. PHASES IN THE KNOWLEDGE DISCOVERY FROM DATABASE PROCESS;22
4.4;4. DIFFERENCES BETWEEN LEGAL AND OTHER DATA;23
4.5;5. CHAPTER SUMMARY;24
5;CHAPTER 2 LEGAL ISSUES IN THE DATA SELECTION PHASE;27
5.1;1. OPEN TEXTURE, DISCRETION AND KDD;27
5.2;2. STARE DECISIS;31
5.3;3. CIVIL AND COMMON LAW C;34
5.4;4. SELECTING A TASK SUITABLE FOR KDD: THE IMPORTANCE OF OPEN TEXTURE;37
5.5;5. SAMPLE ASSESSMENT OF THE DEGREE OF OPEN TEXTURE;42
5.6;6. SELECTING DATASET RECORDS;44
5.7;7. CHAPTER SUMMARY;56
6;CHAPTER 3 LEGAL ISSUES IN THE DATA PRE-PROCESSING PHASE;59
6.1;1. MISSING DATA;59
6.2;2. INCONSISTENT DATA;61
6.3;3. CHAPTER SUMMARY;70
7;CHAPTER 4 LEGAL ISSUES IN THE DATA TRANSFORMATION PHASE;71
7.1;1. AGGREGATING VALUES;72
7.2;2. NORMALISING;73
7.3;3. FEATURE OR EXAMPLE REDUCTION;74
7.4;4. THE USE OF ARGUMENTATION FOR RESTRUCTURING;75
7.5;5. CHAPTER SUMMARY;93
8;CHAPTER 5 DATA MINING WITH RULE INDUCTION;95
8.1;1. RULE INDUCTION WITH ID3;97
8.2;2. USES OF RULE INDUCTION IN LAW;107
8.3;3. CHAPTER SUMMARY;109
9;CHAPTER 6 UNCERTAIN AND STATISTICAL DATA MINING;111
9.1;1. DATA MINING USING ASSOCIATION RULES;111
9.2;2. FUZZY REASONING;123
9.3;3. BAYESIAN CLASSIFICATION;127
9.4;4. CERTAINTY FACTORS;133
9.5;5. NEAREST NEIGHBOUR APPROACHES;134
9.6;6. EVOLUTIONARY COMPUTING AND GENETIC ALGORITHMS;135
9.7;7. KERNEL MACHINES;136
9.8;8. SUPPORT VECTOR MACHINES;137
9.9;9. CHAPTER SUMMARY;139
10;CHAPTER 7 DATA MINING USING NEURAL NETWORKS;141
10.1;1. FEED FORWARD NETWORKS;141
10.2;2. NEURAL NETWORKS IN LAW;152
10.3;3. CHAPTER SUMMARY;157
11;CHAPTER 8 INFORMATION RETRIEVAL AND TEXT MINING;159
11.1;1. INFORMATION RETRIEVAL BASICS;159
11.2;2. INFORMATION RETRIEVAL IN LAW;166
11.3;3. TEXT MINING IN LAW;170
11.4;4. WEB MINING;179
11.5;5. CHAPTER SUMMARY;180
12;CHAPTER 9 EVALUATION, DEPLOYMENT AND RELATED ISSUES;183
12.1;1. GENERALISATION;183
12.2;2. BOOSTING AND BAGGING;191
12.3;3. FRAMEWORKS FOR EVALUATING LEGAL KNOWLEDGE BASED SYSTEMS;192
12.4;4. EXPLANATION;210
12.5;5. SELECTING SUITABLE FIELDS OF LAW;214
12.6;6. LEGAL ONTOLOGIES;216
12.7;7. CHAPTER SUMMARY;221
13;CHAPTER 10 CONCLUSION;223
13.1;1. THE VALIDITY OF USING KDD IN LEGAL DOMAINS;223
13.2;2. KDD AND REASONING WITH CASES;225
13.3;3. WHAT LEGAL DOMAINS ARE AMENABLE TO THE USE OF KDD;226
13.4;4. PREPARING LEGAL DATA FOR USE IN THE KDD PROCESS;228
13.5;5. TECHNIQUES FOR PERFORMING KDD IN LEGAL DATABASES;229
13.6;6. UNDERSTANDING AND JUSTIFYING THE RESULTS OF THE KDD PROCESS;232
13.7;7. HOW KNOWLEDGE DISCOVERY IN LAW CAN ENHANCE ACCESS TO JUSTICE;233
13.8;8. CURRENT AND FUTURE RESEARCH IN KNOWLEDGE DISCOVERY IN LAW;235
14;11 BIBLIOGRAPHY;239
15;12 GLOSSARY;267
16;INDEX;295
