Data Mining for Association Rules and Sequential Patterns

1. Introduction. - 2. Search Space Partition-Based Rule Mining. - 2. 1 Problem Statement. - 2. 2 Search Space. - 2. 3 Splitting Procedure. - 2. 4 Enumerating ?-Frequent Attribute Sets (cass). - 2. 5 Sequential Enumeration Procedure. - 2. 6 Parallel Enumeration Procedure. - 2. 7 Generating the Association Rules. - 3. Apriori and Other Algorithms. - 3. 1 Early Algorithms. - 3. 2 The Apriori Algorithms. - 3. 3 Direct Hashing and Pruning. - 3. 4 Dynamic Set Counting. - 4. Mining for Rules over Attribute Taxonomies. - 4. 1 Association Rules over Taxonomies. - 4. 2 Problem Statement and Algorithms. - 4. 3 Pruning Uninteresting Rules. - 5. Constraint-Based Rule Mining. - 5. 1 Boolean Constraints. - 5. 2 Prime Implicants. - 5. 3 Problem Statement and Algorithms. - 6. Data Partition-Based Rule Mining. - 6. 1 Data Partitioning. - 6. 2 cas Enumeration with Partitioned Data. - 7. Mining for Rules with Categorical and Metric Attributes. - 7. 1 Interval Systems and Quantitative Rules. - 7. 2 k-Partial Completeness. - 7. 3 Pruning Uninteresting Rules. - 7. 4 Enumeration Algorithms. - 8. Optimizing Rules with Quantitative Attributes. - 8. 1 Solving 1-1-Type Rule Optimization Problems. - 8. 2 Solving d-1-Type Rule Optimization Problems. - 8. 3 Solving 1-q-Type Rule Optimization Problems. - 8. 4 Solving d-q-Type Rule Optimization Problems. - 9. Beyond Support-Confidence Framework. - 9. 1 A Criticism of the Support-Confidence Framework. - 9. 2 Conviction. - 9. 3 Pruning Conviction-Based Rules. - 9. 4 One-Step Association Rule Mining. - 9. 6 Refining Conviction: Association Rule Intensity. - 10. Search Space Partition-Based Sequential Pattern Mining. - 10. 1 Problem Statement. - 10. 2 Search Space. - 10. 3 Splitting the Search Space. - 10. 4 Splitting Procedure. - 10. 5 Sequence Enumeration. - Appendix 1. Chernoff Bounds. - Appendix 2. Partitioning in Figure 10. 5: Beyond3rd Power. - Appendix 3. Partitioning in Figure 10. 6: Beyond 3rd Power. - References. Language: English