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Investigation of discovering rules from data.

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.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining and Rule Discovery --- p.1 / Chapter 1.1.1 --- Association Rule --- p.3 / Chapter 1.1.2 --- Sequential Pattern --- p.4 / Chapter 1.1.3 --- Dependence Rule --- p.6 / Chapter 1.2 --- Association Rule Mining --- p.8 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Outline of the Thesis --- p.10 / Chapter 2 --- Related Work on Association Rule Mining --- p.11 / Chapter 2.1 --- Batch Algorithms --- p.11 / Chapter 2.1.1 --- The Apriori Algorithm --- p.11 / Chapter 2.1.2 --- The DIC Algorithm --- p.13 / Chapter 2.1.3 --- The Partition Algorithm --- p.15 / Chapter 2.1.4 --- The Sampling Algorithm --- p.15 / Chapter 2.2 --- Incremental Association Rule Mining --- p.16 / Chapter 2.2.1 --- The FUP Algorithm --- p.17 / Chapter 2.2.2 --- The FUP2 Algorithm --- p.18 / Chapter 2.2.3 --- The FUP* Algorithm --- p.19 / Chapter 2.2.4 --- The Negative Border Method --- p.20 / Chapter 2.2.5 --- Limitations of Existing Incremental Association Rule Mining Algorithms --- p.21 / Chapter 3 --- A New Incremental Association Rule Mining Approach --- p.23 / Chapter 3.1 --- Outline for the Proposed Approach --- p.23 / Chapter 3.2 --- Our New Approach --- p.26 / Chapter 3.2.1 --- The IDIC_M Algorithm --- p.26 / Chapter 3.2.2 --- A Variant Algorithm: The IDIC_S Algorithm --- p.29 / Chapter 3.3 --- Performance Evaluation of Our Approach --- p.30 / Chapter 3.3.1 --- Experimental Results for Algorithm IDIC_M --- p.30 / Chapter 3.3.2 --- Experimental Results for Algorithm IDIC_S --- p.35 / Chapter 3.4 --- Discussion --- p.39 / Chapter 4 --- Related Work on Multiple_Level AR and Belief-Driven Mining --- p.41 / Chapter 4.1 --- Background on Multiple_Level Association Rules --- p.41 / Chapter 4.2 --- Related Work on Multiple-Level Association Rules --- p.42 / Chapter 4.2.1 --- The Basic Algorithm --- p.42 / Chapter 4.2.2 --- The Cumulate Algorithm --- p.44 / Chapter 4.2.3 --- The EstMerge Algorithm --- p.44 / Chapter 4.2.4 --- Using Hierarchy-Information Encoded Transaction Table --- p.45 / Chapter 4.3 --- Background on Rule Mining in the Presence of User Belief --- p.46 / Chapter 4.4 --- Related Work on Rule Mining in the Presence of User Belief --- p.47 / Chapter 4.4.1 --- Post-Analysis of Learned Rules --- p.47 / Chapter 4.4.2 --- Using General Impressions to Analyze Discovered Classification Rules --- p.49 / Chapter 4.4.3 --- A Belief-Driven Method for Discovering Unexpected Patterns --- p.50 / Chapter 4.4.4 --- Constraint-Based Rule Mining --- p.51 / Chapter 4.5 --- Limitations of Existing Approaches --- p.52 / Chapter 5 --- Multiple-Level Association Rules Mining in the Presence of User Belief --- p.54 / Chapter 5.1 --- User Belief Under Taxonomy --- p.55 / Chapter 5.2 --- Formal Definition of Rule Interestingness --- p.57 / Chapter 5.3 --- The MARUB_E Mining Algorithm --- p.61 / Chapter 6 --- Experiments on MARUB_E --- p.64 / Chapter 6.1 --- Preliminary Experiments --- p.64 / Chapter 6.2 --- Experiments on Synthetic Data --- p.68 / Chapter 6.3 --- Experiments on Real Data --- p.71 / Chapter 7 --- Dealing with Vague Belief of User --- p.76 / Chapter 7.1 --- User Belief Under Taxonomy --- p.76 / Chapter 7.2 --- Relationship with Constraint-Based Rule Mining --- p.79 / Chapter 7.3 --- Formal Definition of Rule Interestingness --- p.79 / Chapter 7.4 --- The MARUB_V Mining Algorithm --- p.81 / Chapter 8 --- Experiments on MARUB_V --- p.84 / Chapter 8.1 --- Preliminary Experiments --- p.84 / Chapter 8.1.1 --- Experiments on Synthetic Data --- p.87 / Chapter 8.1.2 --- Experiments on Real Data --- p.93 / Chapter 9 --- Conclusions and Future Work --- p.96 / Chapter 9.1 --- Conclusions --- p.95 / Chapter 9.2 --- Future Work --- p.97

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323268
Date January 2000
ContributorsNg, King Kwok., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xiii, 104 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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