1. Preview: Data Warehousing/Mining. - 1. What is Summary Information?. - 2. Data Information Theory Statistics. - 3. Data Warehousing/Mining Management. - 4. Architecture Tools and Applications. - 5. Conceptual/Practical Mining Tools. - 6. Conclusion. - 2. Data Warehouse Basics. - 1. Methodology. - 2. Conclusion. - 3. CONCEPT OF PATTERNS & VISUALIZATION. - 1. Introduction. - Appendix: Word Problem Solution. - 4. Information Theory & Statistics. - 1. Introduction. - 2. Information Theory. - 3. Variable Interdependence Measure. - 4. Probability Model Comparison. - 5. Pearson's Chi-Square Statistic. - 5. Information and Statistics Linkage. - 1. Statistics. - 2. Concept Of Information. - 3. Information Theory And Statistics. - 4. Conclusion. - 6. Temporal-Spatial Data. - 1. Introduction. - 2. Temporal-Spatial Characteristics. - 3. Temporal-Spatial Data Analysis. - 4. Problem Formulation. - 5. Temperature Analysis Application. - 6. Discussion. - 7. Conclusion. - 7. Change Point Detection Techniques. - 1. Change Point Problem. - 2. Information Criterion Approach. - 3. Binary Segmentation Technique. - 4. Example. - 5. Summary. - 8. Statistical Association Patterns. - 1. Information-Statistical Association. - 2. Conclusion. - 9. Pattern Inference & Model Discovery. - 1. Introduction. - 2. Concept Of Pattern-Based Inference. - 3. Conclusion. - Appendix: Pattern Utility Illustration. - 10. Bayesian Nets & Model Generation. - 1. Preliminary Of Bayesian Networks. - 2. Pattern Synthesis for Model Learning. - 3. Conclusion. - 11. Pattern Ordering Inference: Part I. - 1. Pattern Order Inference Approach. - 2. Bayesian Net Probability Distribution. - 3. Bayesian Model: Pattern Embodiment. - 4. RLCM for Pattern Ordering. - 12. Pattern Ordering Inference: Part II. - 1. Ordering General Event Patterns. - 2. Conclusion. - Appendix I: 51Largest PR(ADHJBCEF % MathType MTEF 2 1 +-% feaagaart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBqj3BWbIqubWexLMBb50ujbqegm0B% 1jxALjharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqr% Ffpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0F% irpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaa% GcbaWaa0aaaeaacaWGhbaaamaamaaabaGaamysaaaaaaa 3B22 $$ \overline G \underline I $$. - Appendix II: Ordering of PR(LI/SE). SE=F G I. - Appendix III. A: Evaluation of Method A. - Appendix III. B: Evaluation of Method B. - Appendix III. C: Evaluation of Method C. - 13. Case Study 1: Oracle Data Warehouse. - 1. Introduction. - 2. Background. - 3. Challenge. - 4. Illustrations. - 5. Conclusion. - Appendix I: Warehouse Data Dictionary. - 14. Case Study 2: Financial Data Analysis. - 1. The Data. - 2. Information Theoretic Approach. - 3. Data Analysis. - 4. Conclusion. - 15. Case Study 3: Forest Classification. - 1. Introduction. - 2. Classifier Model Derivation. - 3. Test Data Characteristics. - 4. Experimental Platform. - 5. Classification Results. - 6. Validation Stage. - 7. Effect of Mixed Data on Performance. - 8. Goodness Measure for Evaluation. - 9. Conclusion. - References. Language: English