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CONTENTS |
6 |
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ACKNOWLEDGEMENTS |
7 |
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PREFACE |
9 |
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CHAPTER 1 INTRODUCTION |
13 |
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1. KNOWLEDGE DISCOVERY FROM DATABASES IN LAW |
14 |
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2. CONCEPTUALALISING DATA |
20 |
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3. PHASES IN THE KNOWLEDGE DISCOVERY FROM DATABASE PROCESS |
22 |
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4. DIFFERENCES BETWEEN LEGAL AND OTHER DATA |
23 |
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5. CHAPTER SUMMARY |
24 |
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CHAPTER 2 LEGAL ISSUES IN THE DATA SELECTION PHASE |
27 |
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1. OPEN TEXTURE, DISCRETION AND KDD |
27 |
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2. STARE DECISIS |
31 |
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3. CIVIL AND COMMON LAW C |
34 |
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4. SELECTING A TASK SUITABLE FOR KDD: THE IMPORTANCE OF OPEN TEXTURE |
37 |
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5. SAMPLE ASSESSMENT OF THE DEGREE OF OPEN TEXTURE |
42 |
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6. SELECTING DATASET RECORDS |
44 |
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7. CHAPTER SUMMARY |
56 |
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CHAPTER 3 LEGAL ISSUES IN THE DATA PRE-PROCESSING PHASE |
59 |
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1. MISSING DATA |
59 |
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2. INCONSISTENT DATA |
61 |
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3. CHAPTER SUMMARY |
70 |
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CHAPTER 4 LEGAL ISSUES IN THE DATA TRANSFORMATION PHASE |
71 |
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1. AGGREGATING VALUES |
72 |
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2. NORMALISING |
73 |
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3. FEATURE OR EXAMPLE REDUCTION |
74 |
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4. THE USE OF ARGUMENTATION FOR RESTRUCTURING |
75 |
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5. CHAPTER SUMMARY |
93 |
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CHAPTER 5 DATA MINING WITH RULE INDUCTION |
95 |
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1. RULE INDUCTION WITH ID3 |
97 |
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2. USES OF RULE INDUCTION IN LAW |
107 |
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3. CHAPTER SUMMARY |
109 |
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CHAPTER 6 UNCERTAIN AND STATISTICAL DATA MINING |
111 |
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1. DATA MINING USING ASSOCIATION RULES |
111 |
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2. FUZZY REASONING |
123 |
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3. BAYESIAN CLASSIFICATION |
127 |
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4. CERTAINTY FACTORS |
133 |
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5. NEAREST NEIGHBOUR APPROACHES |
134 |
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6. EVOLUTIONARY COMPUTING AND GENETIC ALGORITHMS |
135 |
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7. KERNEL MACHINES |
136 |
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8. SUPPORT VECTOR MACHINES |
137 |
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9. CHAPTER SUMMARY |
139 |
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CHAPTER 7 DATA MINING USING NEURAL NETWORKS |
141 |
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1. FEED FORWARD NETWORKS |
141 |
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2. NEURAL NETWORKS IN LAW |
152 |
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3. CHAPTER SUMMARY |
157 |
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CHAPTER 8 INFORMATION RETRIEVAL AND TEXT MINING |
159 |
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1. INFORMATION RETRIEVAL BASICS |
159 |
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2. INFORMATION RETRIEVAL IN LAW |
166 |
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3. TEXT MINING IN LAW |
170 |
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4. WEB MINING |
179 |
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5. CHAPTER SUMMARY |
180 |
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CHAPTER 9 EVALUATION, DEPLOYMENT AND RELATED ISSUES |
183 |
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1. GENERALISATION |
183 |
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2. BOOSTING AND BAGGING |
191 |
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3. FRAMEWORKS FOR EVALUATING LEGAL KNOWLEDGE BASED SYSTEMS |
192 |
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4. EXPLANATION |
210 |
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5. SELECTING SUITABLE FIELDS OF LAW |
214 |
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6. LEGAL ONTOLOGIES |
216 |
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7. CHAPTER SUMMARY |
221 |
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CHAPTER 10 CONCLUSION |
223 |
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1. THE VALIDITY OF USING KDD IN LEGAL DOMAINS |
223 |
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2. KDD AND REASONING WITH CASES |
225 |
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3. WHAT LEGAL DOMAINS ARE AMENABLE TO THE USE OF KDD |
226 |
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4. PREPARING LEGAL DATA FOR USE IN THE KDD PROCESS |
228 |
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5. TECHNIQUES FOR PERFORMING KDD IN LEGAL DATABASES |
229 |
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6. UNDERSTANDING AND JUSTIFYING THE RESULTS OF THE KDD PROCESS |
232 |
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7. HOW KNOWLEDGE DISCOVERY IN LAW CAN ENHANCE ACCESS TO JUSTICE |
233 |
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8. CURRENT AND FUTURE RESEARCH IN KNOWLEDGE DISCOVERY IN LAW |
235 |
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11 BIBLIOGRAPHY |
239 |
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12 GLOSSARY |
267 |
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INDEX |
295 |
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