published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

This thesis focuses on the development of novel data mining techniques that convert user interactions in social media networks into readable data that would benefit users, companies, and governments. The readable data can either warn of dangerous side effects of pharmaceutical drugs or improve intervention strategies. A weighted model enabled us to represent user activity in the network, that allowed us to reflect user sentiment of a pharmaceutical drug and/or service. The result is an accurate representation of user sentiment. This approach, when modified for specific diseases, drugs, and services, can enable rapid user feedback that can be converted into rapid responses from consumers to industry and government to withdraw possibly dangerous drugs and services from the market or improve said drugs and services. Our approach monitors social media networks in real-time, enabling government and industry to rapidly respond to consumer sentiment of pharmaceutical drugs and services. / <p>QC 20170314</p>

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

Li Zhiheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 77-79). / Abstracts in English and Chinese. / Abstract (English) --- p.i / Acknowledgement --- p.iii / Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.ix / List of Algorithms --- p.x / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Review of frequent itemset mining --- p.1 / Chapter 1.2 --- Data mining proxy serving for large numbers of users --- p.3 / Chapter 1.3 --- Privacy issues on proxy service --- p.4 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Frequent itemsets mining --- p.7 / Chapter 2.1 --- Preliminaries --- p.7 / Chapter 2.2 --- Data mining queries --- p.8 / Chapter 2.3 --- A running example --- p.10 / Chapter 3 --- Data Mining Proxy --- p.13 / Chapter 3.1 --- Load data for mining --- p.14 / Chapter 3.2 --- An Overview --- p.16 / Chapter 3.3 --- Tree Operations --- p.16 / Chapter 3.4 --- Data Mining Usages and Observations --- p.18 / Chapter 4 --- Implementation of Proxy --- p.23 / Chapter 4.1 --- Problems in implementation --- p.23 / Chapter 4.2 --- A Coding Scheme --- p.24 / Chapter 4.3 --- On-disk/In-Memory Tree Representations and Mining --- p.27 / Chapter 4.4 --- Tree Operation Implementations --- p.29 / Chapter 4.4.1 --- Tree Projection Operation Implementations: πd2m( )and πm2m( ) --- p.31 / Chapter 4.4.2 --- Tree Merge Operation Implementations: --- p.33 / Chapter 4.4.3 --- Frequent Itemset/Sub-itemset Tree Building Request --- p.37 / Chapter 4.4.4 --- The Tree Projection Operation π and Frequent Super- itemset Tree Building Request --- p.39 / Chapter 5 --- Performance Studies --- p.45 / Chapter 5.1 --- Mining with Different Sizes of Trees in Main Memory --- p.47 / Chapter 5.2 --- Constructing Trees in Main Memory --- p.48 / Chapter 5.3 --- Query Patterns and Number of Queries --- p.50 / Chapter 5.4 --- Testing Sub-itemset Queries with Different Memory Sizes --- p.51 / Chapter 5.5 --- Replacement Strategies --- p.51 / Chapter 6 --- Privacy Preserving in Proxy Service --- p.61 / Chapter 6.1 --- Data Union Regardless Privacy Preserving --- p.61 / Chapter 6.2 --- Secure Data Union --- p.65 / Chapter 6.2.1 --- Secure Multi-party Computation --- p.65 / Chapter 6.2.2 --- Basic Methods of Privacy Preserving in Semi-honest Envi- ronment --- p.67 / Chapter 6.2.3 --- Privacy Preserving On Data Union --- p.70 / Chapter 6.3 --- Discussions --- p.73 / Chapter 7 --- Conclusion --- p.75 / Bibliography --- p.77

Xiaolei Yuan. / Thesis submitted in: December 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 95-101). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Data Mining: A New Wave of Database Applications --- p.1 / Chapter 1.1.2 --- Association Rule Mining --- p.4 / Chapter 1.2 --- Motivation --- p.7 / Chapter 1.3 --- Main Contribution --- p.8 / Chapter 1.4 --- Thesis Organization --- p.9 / Chapter 2 --- Literature Review --- p.10 / Chapter 2.1 --- Data mining and association rule mining --- p.10 / Chapter 2.2 --- Integration data mining with DBMS --- p.11 / Chapter 2.3 --- Query language design for association rule mining --- p.12 / Chapter 2.4 --- Unified data mining models --- p.15 / Chapter 2.5 --- Other topics --- p.15 / Chapter 3 --- A New Data Mining Query Language M2MQL --- p.17 / Chapter 3.1 --- Simple item-based association rule --- p.18 / Chapter 3.1.1 --- One rule set --- p.19 / Chapter 3.1.2 --- Rule set and Source data set --- p.22 / Chapter 3.1.3 --- New rule sets from existing ones --- p.24 / Chapter 3.2 --- Generalized item-based association rules --- p.25 / Chapter 3.3 --- CREATE RULE and SELECT RULE Primitive --- p.32 / Chapter 4 --- The Algebra in M2MQL --- p.33 / Chapter 4.1 --- Review of nested relations --- p.33 / Chapter 4.1.1 --- Concepts of nested relation --- p.34 / Chapter 4.1.2 --- Nested relation and association rule mining --- p.35 / Chapter 4.2 --- Nested relational algebra --- p.36 / Chapter 4.3 --- Specific data mining algebra --- p.39 / Chapter 4.3.1 --- POWERSET p --- p.40 / Chapter 4.3.2 --- SET-CONTAINMENT-JOIN xc --- p.40 / Chapter 4.3.3 --- Functional operators --- p.42 / Chapter 5 --- Mining On Top of M2MQL --- p.50 / Chapter 5.1 --- Problem statement --- p.50 / Chapter 5.2 --- Frequency Counting Phase --- p.52 / Chapter 5.3 --- Frequent Itemset Generation Phase --- p.54 / Chapter 5.4 --- Rule Generation Phase --- p.57 / Chapter 5.5 --- Summary --- p.64 / Chapter 6 --- Conclusions and Future Work --- p.65 / Chapter 6.1 --- What we have achieved --- p.65 / Chapter 6.2 --- What is ahead --- p.66 / Chapter 6.2.1 --- Issues of Query Optimization --- p.66 / Chapter 6.2.2 --- Issues of Expanding Table Forms --- p.67 / Chapter A --- General Syntax of M2MQL --- p.68 / Chapter B --- Syntax and Example for MSQL --- p.71 / Chapter B.1 --- Syntax of MSQL --- p.71 / Chapter B.2 --- Example --- p.73 / Chapter C --- Syntax and Example for MINE RULE --- p.76 / Chapter C.1 --- syntax of MINE RULE --- p.76 / Chapter C.2 --- Example --- p.77 / Chapter C.2.1 --- Counting Groups --- p.78 / Chapter C.2.2 --- Making Couples of Clusters --- p.79 / Chapter C.2.3 --- Extracting Bodies --- p.80 / Chapter C.2.4 --- Extracting Rules --- p.80 / Bibliography --- p.83

Cheung, Yin-ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 117-124). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining Techniques --- p.1 / Chapter 1.1.1 --- Classification --- p.1 / Chapter 1.1.2 --- Association Rules Mining --- p.2 / Chapter 1.1.3 --- Estimation --- p.2 / Chapter 1.1.4 --- Prediction --- p.2 / Chapter 1.1.5 --- Clustering --- p.2 / Chapter 1.1.6 --- Description --- p.3 / Chapter 1.2 --- Problem Definition --- p.3 / Chapter 1.3 --- Thesis Organization --- p.4 / Chapter I --- Decision Tree Classifiers --- p.6 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Introduction to Classification --- p.7 / Chapter 2.2 --- Classification Using Decision Trees --- p.8 / Chapter 2.2.1 --- Constructing a Decision Tree --- p.10 / Chapter 2.2.2 --- Related Work --- p.11 / Chapter 3 --- Strategies to Enhance the Performance in Building Decision Trees --- p.14 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.1.1 --- Related Work --- p.15 / Chapter 3.1.2 --- Post-evaluation vs Pre-evaluation of Splitting Points --- p.19 / Chapter 3.2 --- Schemes to Construct Decision Trees --- p.27 / Chapter 3.2.1 --- One-to-many Hashing --- p.27 / Chapter 3.2.2 --- Many-to-one and Horizontal Hashing --- p.28 / Chapter 3.2.3 --- A Scheme using Paired Attribute Lists --- p.29 / Chapter 3.2.4 --- A Scheme using Database Replication --- p.31 / Chapter 3.3 --- Performance Analysis --- p.32 / Chapter 3.4 --- Experimental Results --- p.38 / Chapter 3.4.1 --- Performance --- p.38 / Chapter 3.4.2 --- Test 1 : Smaller Decision Tree --- p.40 / Chapter 3.4.3 --- Test 2: Bigger Decision Tree --- p.44 / Chapter 3.5 --- Conclusion --- p.47 / Chapter II --- Mining Association Rules --- p.48 / Chapter 4 --- Background --- p.49 / Chapter 4.1 --- Definition --- p.49 / Chapter 4.2 --- Association Algorithms --- p.51 / Chapter 4.2.1 --- Apriori-gen --- p.51 / Chapter 4.2.2 --- Partition --- p.53 / Chapter 4.2.3 --- DIC --- p.54 / Chapter 4.2.4 --- FP-tree --- p.54 / Chapter 4.2.5 --- Vertical Data Mining --- p.58 / Chapter 4.3 --- Taxonomies of Association Rules --- p.58 / Chapter 4.3.1 --- Multi-level Association Rules --- p.58 / Chapter 4.3.2 --- Multi-dimensional Association Rules --- p.59 / Chapter 4.3.3 --- Quantitative Association Rules --- p.59 / Chapter 4.3.4 --- Random Sampling --- p.60 / Chapter 4.3.5 --- Constraint-based Association Rules --- p.60 / Chapter 5 --- Mining Association Rules without Support Thresholds --- p.62 / Chapter 5.1 --- Introduction --- p.63 / Chapter 5.1.1 --- Itemset-Loop --- p.66 / Chapter 5.2 --- New Approaches --- p.67 / Chapter 5.2.1 --- "A Build-Once and Mine-Once Approach, BOMO" --- p.68 / Chapter 5.2.2 --- "A Loop-back Approach, LOOPBACK" --- p.74 / Chapter 5.2.3 --- "A Build-Once and Loop-Back Approach, BOLB" --- p.77 / Chapter 5.2.4 --- Discussion --- p.77 / Chapter 5.3 --- Generalization: Varying Thresholds Nk for k-itemsets --- p.78 / Chapter 5.4 --- Performance Evaluation --- p.78 / Chapter 5.4.1 --- Generalization: Varying Nk for k-itemsets --- p.84 / Chapter 5.4.2 --- Non-optimal Thresholds --- p.84 / Chapter 5.4.3 --- "Different Decrease Factors,f" --- p.85 / Chapter 5.5 --- Conclusion --- p.87 / Chapter 6 --- Mining Interesting Itemsets with Item Constraints --- p.88 / Chapter 6.1 --- Introduction --- p.88 / Chapter 6.2 --- Proposed Algorithms --- p.91 / Chapter 6.2.1 --- Single FP-tree Approach --- p.92 / Chapter 6.2.2 --- Double FP-trees Approaches --- p.93 / Chapter 6.3 --- Maximum Support Thresholds --- p.102 / Chapter 6.4 --- Performance Evaluation --- p.103 / Chapter 6.5 --- Conclusion --- p.109 / Chapter 7 --- Conclusion --- p.110 / Chapter A --- Probabilistic Analysis of Hashing Schemes --- p.112 / Chapter B --- Hash Functions --- p.114 / Bibliography --- p.117

No description available.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Univ., Diss.--Frankfurt am Main, 2007.

Univ., Diss--Würzburg, 2006.