Investigation of discovering rules from data.

Abstract

by Ng, King Kwok.Thesis submitted in: December 1999.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 99-104).Abstracts in English and Chinese.Acknowledgments --- p.iiAbstract --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining and Rule Discovery --- p.1Chapter 1.1.1 --- Association Rule --- p.3Chapter 1.1.2 --- Sequential Pattern --- p.4Chapter 1.1.3 --- Dependence Rule --- p.6Chapter 1.2 --- Association Rule Mining --- p.8Chapter 1.3 --- Contributions --- p.9Chapter 1.4 --- Outline of the Thesis --- p.10Chapter 2 --- Related Work on Association Rule Mining --- p.11Chapter 2.1 --- Batch Algorithms --- p.11Chapter 2.1.1 --- The Apriori Algorithm --- p.11Chapter 2.1.2 --- The DIC Algorithm --- p.13Chapter 2.1.3 --- The Partition Algorithm --- p.15Chapter 2.1.4 --- The Sampling Algorithm --- p.15Chapter 2.2 --- Incremental Association Rule Mining --- p.16Chapter 2.2.1 --- The FUP Algorithm --- p.17Chapter 2.2.2 --- The FUP2 Algorithm --- p.18Chapter 2.2.3 --- The FUP* Algorithm --- p.19Chapter 2.2.4 --- The Negative Border Method --- p.20Chapter 2.2.5 --- Limitations of Existing Incremental Association Rule Mining Algorithms --- p.21Chapter 3 --- A New Incremental Association Rule Mining Approach --- p.23Chapter 3.1 --- Outline for the Proposed Approach --- p.23Chapter 3.2 --- Our New Approach --- p.26Chapter 3.2.1 --- The IDIC_M Algorithm --- p.26Chapter 3.2.2 --- A Variant Algorithm: The IDIC_S Algorithm --- p.29Chapter 3.3 --- Performance Evaluation of Our Approach --- p.30Chapter 3.3.1 --- Experimental Results for Algorithm IDIC_M --- p.30Chapter 3.3.2 --- Experimental Results for Algorithm IDIC_S --- p.35Chapter 3.4 --- Discussion --- p.39Chapter 4 --- Related Work on Multiple_Level AR and Belief-Driven Mining --- p.41Chapter 4.1 --- Background on Multiple_Level Association Rules --- p.41Chapter 4.2 --- Related Work on Multiple-Level Association Rules --- p.42Chapter 4.2.1 --- The Basic Algorithm --- p.42Chapter 4.2.2 --- The Cumulate Algorithm --- p.44Chapter 4.2.3 --- The EstMerge Algorithm --- p.44Chapter 4.2.4 --- Using Hierarchy-Information Encoded Transaction Table --- p.45Chapter 4.3 --- Background on Rule Mining in the Presence of User Belief --- p.46Chapter 4.4 --- Related Work on Rule Mining in the Presence of User Belief --- p.47Chapter 4.4.1 --- Post-Analysis of Learned Rules --- p.47Chapter 4.4.2 --- Using General Impressions to Analyze Discovered Classification Rules --- p.49Chapter 4.4.3 --- A Belief-Driven Method for Discovering Unexpected Patterns --- p.50Chapter 4.4.4 --- Constraint-Based Rule Mining --- p.51Chapter 4.5 --- Limitations of Existing Approaches --- p.52Chapter 5 --- Multiple-Level Association Rules Mining in the Presence of User Belief --- p.54Chapter 5.1 --- User Belief Under Taxonomy --- p.55Chapter 5.2 --- Formal Definition of Rule Interestingness --- p.57Chapter 5.3 --- The MARUB_E Mining Algorithm --- p.61Chapter 6 --- Experiments on MARUB_E --- p.64Chapter 6.1 --- Preliminary Experiments --- p.64Chapter 6.2 --- Experiments on Synthetic Data --- p.68Chapter 6.3 --- Experiments on Real Data --- p.71Chapter 7 --- Dealing with Vague Belief of User --- p.76Chapter 7.1 --- User Belief Under Taxonomy --- p.76Chapter 7.2 --- Relationship with Constraint-Based Rule Mining --- p.79Chapter 7.3 --- Formal Definition of Rule Interestingness --- p.79Chapter 7.4 --- The MARUB_V Mining Algorithm --- p.81Chapter 8 --- Experiments on MARUB_V --- p.84Chapter 8.1 --- Preliminary Experiments --- p.84Chapter 8.1.1 --- Experiments on Synthetic Data --- p.87Chapter 8.1.2 --- Experiments on Real Data --- p.93Chapter 9 --- Conclusions and Future Work --- p.96Chapter 9.1 --- Conclusions --- p.95Chapter 9.2 --- Future Work --- p.9