| Introduction | p. 1 |
| What Is Data Mining? | p. 1 |
| Why Do We Need Data Mining? | p. 2 |
| Knowledge Discovery in Databases (KDD) | p. 4 |
| Processing Steps of KDD | p. 4 |
| Feature Selection | p. 6 |
| Applications of Knowledge Discovery in Databases | p. 7 |
| Data Mining Task | p. 7 |
| Data Mining Techniques | p. 9 |
| Clustering | p. 9 |
| Classification | p. 10 |
| Conceptual Clustering and Classification | p. 14 |
| Dependency Modeling | p. 15 |
| Summarization | p. 15 |
| Regression | p. 16 |
| Case-Based Learning | p. 16 |
| Mining Time-Series Data | p. 17 |
| Data Mining and Marketing | p. 17 |
| Solving Real-World Problems by Data Mining | p. 18 |
| Summary | p. 21 |
| Trends of Data Mining | p. 21 |
| Outline | p. 22 |
| Association Rule | p. 25 |
| Basic Concepts | p. 25 |
| Measurement of Association Rules | p. 30 |
| Support-Confidence Framework | p. 30 |
| Three Established Measurements | p. 31 |
| Searching Frequent Itemsets | p. 33 |
| The Apriori Algorithm | p. 33 |
| Identifying Itemsets of Interest | p. 36 |
| Research into Mining Association Rules | p. 39 |
| Chi-squared Test Method | p. 40 |
| The FP-tree Based Model | p. 43 |
| OPUS Based Algorithm | p. 44 |
| Summary | p. 46 |
| Negative Association Rule | p. 47 |
| Introduction | p. 47 |
| Focusing on Itemsets of Interest | p. 51 |
| Effectiveness of Focusing on Infrequent Itemsets of Interest | p. 53 |
| Itemsets of Interest | p. 55 |
| Positive Itemsets of Interest | p. 55 |
| Negative Itemsets of Interest | p. 58 |
| Searching Interesting Itemsets | p. 59 |
| Procedure | p. 59 |
| An Example | p. 62 |
| A Twice-Pruning Approach | p. 65 |
| Negative Association Rules of Interest | p. 66 |
| Measurement | p. 66 |
| Examples | p. 71 |
| Algorithms Design | p. 73 |
| Identifying Reliable Exceptions | p. 75 |
| Confidence Based Interestingness | p. 75 |
| Support Based Interestingness | p. 77 |
| Searching Reliable Exceptions | p. 78 |
| Comparisons | p. 80 |
| Comparison with Support-Confidence Framework | p. 80 |
| Comparison with Interest Models | p. 80 |
| Comparison with Exception Mining Model | p. 81 |
| Comparison with Strong Negative Association Model | p. 82 |
| Summary | p. 83 |
| Causality in Databases | p. 85 |
| Introduction | p. 85 |
| Basic Definitions | p. 87 |
| Data Partitioning | p. 90 |
| Partitioning Domains of Attributes | p. 90 |
| Quantitative Items | p. 92 |
| Decomposition and Composition of Quantitative Items | p. 93 |
| Item Variables | p. 95 |
| Decomposition and Composition for Item Variables | p. 96 |
| Procedure of Partitioning | p. 98 |
| Dependency among Variables | p. 99 |
| Conditional Probabilities | p. 100 |
| Causal Rules of Interest | p. 101 |
| Algorithm Design | p. 103 |
| Causality in Probabilistic Databases | p. 105 |
| Problem Statement | p. 105 |
| Required Concepts | p. 108 |
| Preprocess of Data | p. 108 |
| Probabilistic Dependency | p. 110 |
| Improvements | p. 115 |
| Summary | p. 119 |
| Causal Rule Analysis | p. 121 |
| Introduction | p. 121 |
| Problem Statement | p. 122 |
| Related Concepts | p. 124 |
| Optimizing Causal Rules | p. 126 |
| Unnecessary Information | p. 126 |
| Merging Unnecessary Information | p. 127 |
| Merging Items with Identical Properties | p. 130 |
| Polynomial Function for Causality | p. 131 |
| Causal Relationship | p. 132 |
| Binary Linear Causality | p. 132 |
| N-ary Linear Propagating Model | p. 137 |
| Examples | p. 139 |
| Functions for General Causality | p. 143 |
| Approximating Causality by Fitting | p. 149 |
| Preprocessing of Data | p. 149 |
| Constructing the Polynomial Function | p. 150 |
| Algorithm Design | p. 155 |
| Examples | p. 156 |
| Summary | p. 159 |
| Association Rules in Very Large Databases | p. 161 |
| Introduction | p. 161 |
| Instance Selection | p. 164 |
| Evaluating the Size of Instance Sets | p. 164 |
| Generating Instance Set | p. 167 |
| Estimation of Association Rules | p. 169 |
| Identifying Approximate Frequent Itemsets | p. 169 |
| Measuring Association Rules of Interest | p. 171 |
| Algorithm Designing | p. 172 |
| Searching True Association Rules Based on Approximations | p. 173 |
| Incremental Mining | p. 179 |
| Promising Itemsets | p. 180 |
| Searching Procedure | p. 182 |
| Competitive Set Method | p. 187 |
| Assigning Weights | p. 188 |
| Algorithm of Incremental Mining | p. 190 |
| Improvement of Incremental Mining | p. 193 |
| Conditions of Termination | p. 193 |
| Anytime Search Algorithm | p. 194 |
| Summary | p. 197 |
| Association Rules in Small Databases | p. 199 |
| Introduction | p. 200 |
| Problem Statement | p. 201 |
| Problems Faced by Utilizing External Data | p. 201 |
| Our Approach | p. 203 |
| External Data Collecting | p. 204 |
| Available Tools | p. 204 |
| Indexing by a Conditional Associated Semantic | p. 206 |
| Procedures for Similarity | p. 208 |
| A Data Preprocessing Framework | p. 209 |
| Pre-analysis: Selecting Relevant and Uncontradictable Collected Data-Sources | p. 209 |
| Post-analysis: Summarizing Historical Data | p. 212 |
| Algorithm Designing | p. 214 |
| Synthesizing Selected Rules | p. 217 |
| Assigning Weights | p. 218 |
| Algorithm Design | p. 221 |
| Refining Rules Mined in Small Databases | p. 222 |
| Summary | p. 223 |
| Conclusion and Future Work | p. 225 |
| Conclusion | p. 225 |
| Future Work | p. 226 |
| References | p. 229 |
| Subject Index | p. 237 |
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