by Chi-ming Wat. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 77-82). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Performance analysis of on-line algorithms --- p.2 / Chapter 1.2 --- Randomized algorithms --- p.4 / Chapter 1.3 --- Types of adversaries --- p.5 / Chapter 1.4 --- Overview of the results --- p.6 / Chapter 2 --- The k-server problem --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- Related Work --- p.9 / Chapter 2.3 --- The Evolution of Work Function Algorithm --- p.12 / Chapter 2.4 --- Definitions --- p.16 / Chapter 2.5 --- The Work Function Algorithm --- p.18 / Chapter 2.6 --- The Competitive Analysis --- p.20 / Chapter 3 --- The weighted k-server problem --- p.27 / Chapter 3.1 --- Introduction --- p.27 / Chapter 3.2 --- Related Work --- p.29 / Chapter 3.3 --- Fiat and Ricklin's Algorithm --- p.29 / Chapter 3.4 --- The Work Function Algorithm --- p.32 / Chapter 3.5 --- The Competitive Analysis --- p.35 / Chapter 4 --- The Influence of Lookahead --- p.41 / Chapter 4.1 --- Introduction --- p.41 / Chapter 4.2 --- Related Work --- p.42 / Chapter 4.3 --- The Role of l-lookahead --- p.43 / Chapter 4.4 --- The LRU Algorithm with l-lookahead --- p.45 / Chapter 4.5 --- The Competitive Analysis --- p.45 / Chapter 5 --- Space Complexity --- p.57 / Chapter 5.1 --- Introduction --- p.57 / Chapter 5.2 --- Related Work --- p.59 / Chapter 5.3 --- Preliminaries --- p.59 / Chapter 5.4 --- The TWO Algorithm --- p.60 / Chapter 5.5 --- Competitive Analysis --- p.61 / Chapter 5.6 --- Remarks --- p.69 / Chapter 6 --- Conclusions --- p.70 / Chapter 6.1 --- Summary of Our Results --- p.70 / Chapter 6.2 --- Recent Results --- p.71 / Chapter 6.2.1 --- The Adversary Models --- p.71 / Chapter 6.2.2 --- On-line Performance-Improvement Algorithms --- p.73 / Chapter A --- Proof of Lemma1 --- p.75 / Bibliography --- p.77

by Cheang Sin Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 137-140. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Lisp as an AI Programming Language --- p.1 / Chapter 1.2 --- Assisting List Processing with Hardware --- p.2 / Chapter 1.3 --- Simulation Study --- p.2 / Chapter 1.4 --- Implementation --- p.3 / Chapter 1.4.1 --- Hardware --- p.3 / Chapter 1.4.2 --- Software --- p.3 / Chapter 1.5 --- Performance --- p.4 / Chapter CHAPTER 2 --- LISP AND EXISTING LISP MACHINES --- p.5 / Chapter 2.1 --- Lisp and its Internal Structure --- p.5 / Chapter 2.1.1 --- The List Structure in Lisp --- p.5 / Chapter 2.1.2 --- Data Types in Lisp --- p.7 / Chapter 2.1.3 --- Lisp Functions --- p.8 / Chapter 2.1.4 --- Storage Management of Lisp --- p.9 / Chapter 2.2 --- Existing Lisp Machines --- p.11 / Chapter 2.2.1 --- Types of AI Architecture --- p.11 / Language-Based architecture --- p.11 / Knowledge-Based architecture --- p.12 / Semantic networks --- p.12 / Chapter 2.2.2 --- Lisp Machines --- p.12 / Solving problems of Lisp --- p.13 / Chapter 2.2.3 --- Classes of Lisp Machines --- p.14 / Two M Lisp machine examples --- p.15 / A class P machine example --- p.17 / A class S machine example --- p.17 / The best class for Lisp --- p.19 / Chapter 2.3 --- Execution Time Analysis of a Lisp System --- p.20 / Chapter 2.3.1 --- CPU Time Statistics --- p.20 / Chapter 2.3.2 --- Statistics Analysis --- p.24 / Chapter CHAPTER 3 --- OVERALL ARCHITECTURE OF THE ASLP --- p.27 / Chapter 3.1 --- An Arithmetical & Symbolical List Processor --- p.27 / Chapter 3.2 --- Multiple Memory Modules --- p.30 / Chapter 3.3 --- Large Number of Registers --- p.31 / Chapter 3.4 --- Multiple Buses --- p.34 / Chapter 3.5 --- Special Function Units --- p.35 / Chapter CHAPTER 4 --- PARALLELISM IN THE ASLP --- p.36 / Chapter 4.1 --- Parallel Data Movement --- p.36 / Chapter 4.2 --- Wide Memory Modules --- p.37 / Chapter 4.3 --- Parallel Memory Access --- p.39 / Chapter 4.3.1 --- Parallelism and Pipelining --- p.39 / Chapter 4.4 --- Pipelined Micro-Instructions --- p.40 / Chapter 4.4.1 --- Memory access pipelining --- p.41 / Chapter 4.5 --- Performance Estimation --- p.44 / Chapter 4.6 --- Parallel Execution with the Host Computer --- p.45 / Chapter CHAPTER 5 --- SIMULATION STUDY OF THE ASLP --- p.47 / Chapter 5.1 --- Why Simulation is needed for the ASLP? --- p.47 / Chapter 5.2 --- The Structure of the HOCB Simulator --- p.48 / Chapter 5.2.1 --- Activity-Oriented Simulation for the ASLP --- p.50 / Chapter 5.3 --- The Hardware Object Declaration Method --- p.50 / Chapter 5.4 --- A Register-Level Simulation of the ASLP --- p.53 / Chapter 5.4.1 --- A List Function Simulation --- p.54 / Chapter CHAPTER 6 --- DESIGN AND IMPLEMENTATION OF THE ASLP --- p.57 / Chapter 6.1 --- Hardware --- p.57 / Chapter 6.1.1 --- Microprogrammable Controller --- p.57 / The instruction cycle of the micro-controller --- p.59 / Chapter 6.1.2 --- Chip Selection and Allocation --- p.59 / Chapter 6.2 --- Software --- p.61 / Chapter 6.2.1 --- Instruction Passing --- p.61 / Chapter 6.2.2 --- Microprogram Development --- p.62 / Microprogram field definition --- p.64 / Micro-assembly language --- p.65 / Macro-instructions --- p.65 / Down-loading of Micro-Codes --- p.66 / Interfacing to C language --- p.66 / A Turbo C Function Library --- p.67 / Chapter CHAPTER 7 --- PERFORMANCE EVALUATION OF THE ASLP …… --- p.68 / Chapter 7.1 --- Micro-Functions in the ASLP --- p.68 / Chapter 7.2 --- Functions in the C Library --- p.71 / Chapter CHAPTER 8 --- FUNCTIONAL EVALUATION OF THE ASLP --- p.77 / Chapter 8.1 --- A Relational Database on the ASLP --- p.77 / Chapter 8.1.1 --- Data Representation --- p.77 / Chapter 8.1.2 --- Performance of the Database System --- p.79 / Chapter 8.2 --- Other Potential Applications --- p.80 / Chapter CHAPTER 9 --- FUTURE DEVELOPMENT OF THE ASLP --- p.81 / Chapter 9.1 --- An Expert System Shell on the ASLP --- p.81 / Chapter 9.1.1 --- Definition of Objects --- p.81 / Chapter 9.1.2 --- Knowledge Representation --- p.84 / Chapter 9.1.3 --- Knowledge Representation in the ASLP --- p.85 / Chapter 9.1.4 --- Overall Structure --- p.88 / Chapter 9.2 --- Reducing the Physical Size by Employing VLSIs --- p.89 / Chapter CHAPTER 10 --- CONCLUSION --- p.92 / Chapter APPENDIX A --- BLOCK DIAGRAM --- p.95 / Chapter APPENDIX B --- ASLP CIRCUIT DIAGRAMS --- p.97 / Chapter APPENDIX C --- ASLP PC-BOARD LAYOUTS --- p.114 / Chapter APPENDIX D --- MICRO-CONTROL SIGNAL ASSIGNMENT --- p.121 / Chapter APPENDIX E --- MICRO-FIELD DEFINITION --- p.124 / Chapter APPENDIX F --- MACRO DEFINITION --- p.133 / Chapter APPENDIX G --- REGISTER ASSIGNMENT --- p.134 / PUBLICATIONS --- p.136 / REFERENCES --- p.137

Kam, Po-shan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 89-97). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Temporal Data Management --- p.2 / Chapter 1.3 --- Temporal reasoning and temporal semantics --- p.3 / Chapter 1.4 --- Temporal Data Mining --- p.5 / Chapter 1.5 --- Motivation --- p.6 / Chapter 1.6 --- Approach --- p.7 / Chapter 1.6.1 --- Focus and Objectives --- p.8 / Chapter 1.6.2 --- Experimental Setup --- p.8 / Chapter 1.7 --- Outline and contributions --- p.9 / Chapter 2 --- Relevant Work --- p.10 / Chapter 2.1 --- Data Mining --- p.10 / Chapter 2.1.1 --- Association Rules --- p.13 / Chapter 2.1.2 --- Classification --- p.15 / Chapter 2.1.3 --- Clustering --- p.16 / Chapter 2.2 --- Sequential Pattern --- p.17 / Chapter 2.2.1 --- Frequent Patterns --- p.18 / Chapter 2.2.2 --- Interesting Patterns --- p.20 / Chapter 2.2.3 --- Granularity --- p.21 / Chapter 2.3 --- Temporal Database --- p.21 / Chapter 2.4 --- Temporal Reasoning --- p.23 / Chapter 2.4.1 --- Natural Language Expression --- p.24 / Chapter 2.4.2 --- Temporal Logic Approach --- p.25 / Chapter 2.5 --- Temporal Data Mining --- p.25 / Chapter 2.5.1 --- Framework --- p.25 / Chapter 2.5.2 --- Temporal Association Rules --- p.26 / Chapter 2.5.3 --- Attribute-Oriented Induction --- p.27 / Chapter 2.5.4 --- Time Series Analysis --- p.27 / Chapter 3 --- Discovering Temporal Patterns for interval-based events --- p.29 / Chapter 3.1 --- Temporal Database --- p.29 / Chapter 3.2 --- Allen's Taxonomy of Temporal Relationships --- p.31 / Chapter 3.3 --- "Mining Temporal Pattern, AppSeq and LinkSeq" --- p.33 / Chapter 3.3.1 --- A1 and A2 temporal pattern --- p.33 / Chapter 3.3.2 --- "Second Temporal Pattern, LinkSeq" --- p.34 / Chapter 3.4 --- Overview of the Framework --- p.35 / Chapter 3.4.1 --- "Mining Temporal Pattern I, AppSeq" --- p.36 / Chapter 3.4.2 --- "Mining Temporal Pattern II, LinkSeq" --- p.36 / Chapter 3.5 --- Summary --- p.37 / Chapter 4 --- "Mining Temporal Pattern I, AppSeq" --- p.38 / Chapter 4.1 --- Problem Statement --- p.38 / Chapter 4.2 --- Mining A1 Temporal Patterns --- p.40 / Chapter 4.2.1 --- Candidate Generation --- p.43 / Chapter 4.2.2 --- Large k-Items Generation --- p.46 / Chapter 4.3 --- Mining A2 Temporal Patterns --- p.48 / Chapter 4.3.1 --- Candidate Generation: --- p.49 / Chapter 4.3.2 --- Generating Large 2k-Items: --- p.51 / Chapter 4.4 --- Modified AppOne and AppTwo --- p.51 / Chapter 4.5 --- Performance Study --- p.53 / Chapter 4.5.1 --- Experimental Setup --- p.53 / Chapter 4.5.2 --- Experimental Results --- p.54 / Chapter 4.5.3 --- Medical Data --- p.58 / Chapter 4.6 --- Summary --- p.60 / Chapter 5 --- "Mining Temporal Pattern II, LinkSeq" --- p.62 / Chapter 5.1 --- Problem Statement --- p.62 / Chapter 5.2 --- "First Method for Mining LinkSeq, LinkApp" --- p.63 / Chapter 5.3 --- "Second Method for Mining LinkSeq, LinkTwo" --- p.64 / Chapter 5.4 --- "Alternative Method for Mining LinkSeq, LinkTree" --- p.65 / Chapter 5.4.1 --- Sequence Tree: Design --- p.65 / Chapter 5.4.2 --- Construction of seq-tree --- p.69 / Chapter 5.4.3 --- Mining LinkSeq using seq-tree --- p.76 / Chapter 5.5 --- Performance Study --- p.82 / Chapter 5.6 --- Discussions --- p.85 / Chapter 5.7 --- Summary --- p.85 / Chapter 6 --- Conclusion and Future Work --- p.87 / Chapter 6.1 --- Conclusion --- p.87 / Chapter 6.2 --- Future Work --- p.88 / Bibliography --- p.97

Law Yee-shan, Carol. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 81-89). / Abstracts in English and Chinese. / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Research Contributions --- p.5 / Chapter 1.2 --- Organization of the thesis --- p.5 / Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.7 / Chapter 2.1 --- Temporal data Association --- p.7 / Chapter 2.1.1 --- Association Rule Mining --- p.8 / Chapter 2.1.2 --- Sequential Patterns Mining --- p.10 / Chapter 2.2 --- Information Retrieval Techniques --- p.11 / Chapter 2.2.1 --- Vector Space model --- p.12 / Chapter 2.2.2 --- Probabilistic model --- p.75 / Chapter CHAPTER THREE --- AN OVERVIEW OF THE PROPOSED APPROACH --- p.16 / Chapter 3.1 --- The Test Bed --- p.19 / Chapter 3.2 --- General Concept Term Identification........................................……… --- p.19 / Chapter 3.3 --- Anchor Document Selection --- p.21 / Chapter 3.4 --- Specific Concept Term Identification --- p.22 / Chapter 3.5 --- Establishment of Associations --- p.22 / Chapter CHAPTER FOUR --- GENERAL CONCEPT TERM IDENTIFICATION --- p.24 / Chapter 4.1 --- Document Pre-processing --- p.25 / Chapter 4.2 --- Stopwording and stemming --- p.29 / Chapter 4.3 --- Word-phrase formation --- p.29 / Chapter 4.4 --- Automatic Indexing of Words and Sentences --- p.30 / Chapter 4.5 --- Relevance Weighting --- p.31 / Chapter 4.5.1 --- Term Frequency and Document Frequency Computation --- p.31 / Chapter 4.5.2 --- Uncommon Data Removal --- p.32 / Chapter 4.5.3 --- Combined Weight Computation --- p.32 / Chapter 4.5.4 --- Cluster Analysis --- p.33 / Chapter 4.6 --- Hopfield Network Classification --- p.35 / Chapter CHAPTER FIVE --- ANCHOR DOCUMENT SELECTION --- p.37 / Chapter 5.1 --- What is an anchor document? --- p.37 / Chapter 5.2 --- Selection Criteria of an anchor document --- p.40 / Chapter CHAPTER SIX --- DISCOVERY OF NEWS ASSOCIATION --- p.44 / Chapter 6.1 --- Specific Concept Term Identification --- p.44 / Chapter 6.2 --- Establishment of Associations --- p.45 / Chapter 6.2.1 --- Anchor document representation --- p.46 / Chapter 6.2.2 --- Similarity measurement --- p.47 / Chapter 6.2.3 --- Formation of a link of news --- p.48 / Chapter CHAPTER SEVEN --- EXPERIMENTAL RESULTS AND ANALYSIS --- p.54 / Chapter 7.1 --- Objective of Experiments --- p.54 / Chapter 7.2 --- Background of Subjects --- p.55 / Chapter 7.3 --- Design of Experiments --- p.55 / Chapter 7.3.1 --- Experimental Data --- p.55 / Chapter 7.3.2 --- Methodology --- p.55 / Anchor document selection --- p.57 / Specific concept term identification --- p.55 / News association --- p.59 / Chapter 7.4 --- Results and Analysis --- p.60 / Anchor document selection --- p.60 / Specific concept term identification --- p.64 / News association --- p.68 / Chapter CHAPTER EIGHT --- CONCLUSIONS AND FUTURE WORK --- p.72 / Chapter 8.1 --- Conclusions --- p.72 / Chapter 8.2 --- Future work --- p.74 / APPENDIX A --- p.76 / APPENDIX B --- p.78 / BIBLIOGRAPHY --- p.81

by Kwong, Wang-Wai Renfrew. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 102-105). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining Tasks --- p.1 / Chapter 1.1.1 --- Characterization --- p.2 / Chapter 1.1.2 --- Discrimination --- p.2 / Chapter 1.1.3 --- Classification --- p.2 / Chapter 1.1.4 --- Clustering --- p.3 / Chapter 1.1.5 --- Prediction --- p.3 / Chapter 1.1.6 --- Description --- p.3 / Chapter 1.1.7 --- Association Rule Mining --- p.4 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.2.1 --- Motivation for Mining Multi-level Association Rules Using Data Cubes --- p.4 / Chapter 1.2.2 --- Motivation for Mining N-most Interesting Itemsets --- p.8 / Chapter 1.3 --- Outline of the Thesis --- p.10 / Chapter 2 --- Survey on Previous Work --- p.11 / Chapter 2.1 --- Data Warehousing --- p.11 / Chapter 2.1.1 --- Data Cube --- p.12 / Chapter 2.2 --- Data Mining --- p.13 / Chapter 2.2.1 --- Association Rules --- p.14 / Chapter 2.2.2 --- Multi-level Association Rules --- p.15 / Chapter 2.2.3 --- Multi-Dimensional Association Rules Using Data Cubes --- p.16 / Chapter 2.2.4 --- Apriori Algorithm --- p.19 / Chapter 3 --- Mining Multi-level Association Rules Using Data Cubes --- p.22 / Chapter 3.1 --- Use of Multi-level Concept --- p.22 / Chapter 3.1.1 --- Multi-level Concept --- p.22 / Chapter 3.1.2 --- Criteria of Using Multi-level Concept --- p.23 / Chapter 3.1.3 --- Use of Multi-level Concept in Association Rules --- p.24 / Chapter 3.2 --- Use of Data Cube --- p.25 / Chapter 3.2.1 --- Data Cube --- p.25 / Chapter 3.2.2 --- Mining Multi-level Association Rules Using Data Cubes --- p.26 / Chapter 3.2.3 --- Definition --- p.28 / Chapter 3.3 --- Method for Mining Multi-level Association Rules Using Data Cubes --- p.31 / Chapter 3.3.1 --- Algorithm --- p.33 / Chapter 3.3.2 --- Example --- p.35 / Chapter 3.4 --- Experiment --- p.44 / Chapter 3.4.1 --- Simulation of Data Cube by Array --- p.44 / Chapter 3.4.2 --- Simulation of Data Cube by B+ Tree --- p.48 / Chapter 3.5 --- Discussion --- p.54 / Chapter 4 --- Mining the N-most Interesting Itemsets --- p.56 / Chapter 4.1 --- Mining the N-most Interesting Itemsets --- p.56 / Chapter 4.1.1 --- Criteria of Mining the N-most Interesting itemsets --- p.56 / Chapter 4.1.2 --- Definition --- p.58 / Chapter 4.1.3 --- Property --- p.59 / Chapter 4.2 --- Method for Mining N-most Interesting Itemsets --- p.60 / Chapter 4.2.1 --- Algorithm --- p.60 / Chapter 4.2.2 --- Example --- p.76 / Chapter 4.3 --- Experiment --- p.81 / Chapter 4.3.1 --- Synthetic Data --- p.81 / Chapter 4.3.2 --- Real Data --- p.85 / Chapter 4.4 --- Discussion --- p.98 / Chapter 5 --- Conclusion --- p.100 / Bibliography --- p.101 / Appendix --- p.106 / Chapter A --- Programs for Mining the N-most Interesting Itemset --- p.106 / Chapter A.1 --- Programs --- p.106 / Chapter A.2 --- Data Structures --- p.108 / Chapter A.3 --- Global Variables --- p.109 / Chapter A.4 --- Functions --- p.110 / Chapter A.5 --- Result Format --- p.113 / Chapter B --- Programs for Mining the Multi-level Association Rules Using Data Cube --- p.114 / Chapter B.1 --- Programs --- p.114 / Chapter B.2 --- Data Structure --- p.118 / Chapter B.3 --- Variables --- p.118 / Chapter B.4 --- Functions --- p.119

Ng Ka Ka. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 114-120). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.vii / Chapter I --- Projective Clustering --- p.1 / Chapter 1 --- Introduction to Projective Clustering --- p.2 / Chapter 2 --- Related Work to Projective Clustering --- p.7 / Chapter 2.1 --- CLARANS - Graph Abstraction and Bounded Optimization --- p.8 / Chapter 2.1.1 --- Graph Abstraction --- p.8 / Chapter 2.1.2 --- Bounded Optimized Random Search --- p.9 / Chapter 2.2 --- OptiGrid ´ؤ Grid Partitioning Approach and Density Estimation Function --- p.9 / Chapter 2.2.1 --- Empty Space Phenomenon --- p.10 / Chapter 2.2.2 --- Density Estimation Function --- p.11 / Chapter 2.2.3 --- Upper Bound Property --- p.12 / Chapter 2.3 --- CLIQUE and ENCLUS - Subspace Clustering --- p.13 / Chapter 2.3.1 --- Monotonicity Property of Subspaces --- p.14 / Chapter 2.4 --- PROCLUS Projective Clustering --- p.15 / Chapter 2.5 --- ORCLUS - Generalized Projective Clustering --- p.16 / Chapter 2.5.1 --- Singular Value Decomposition SVD --- p.17 / Chapter 2.6 --- "An ""Optimal"" Projective Clustering" --- p.17 / Chapter 3 --- EPC : Efficient Projective Clustering --- p.19 / Chapter 3.1 --- Motivation --- p.19 / Chapter 3.2 --- Notations and Definitions --- p.21 / Chapter 3.2.1 --- Density Estimation Function --- p.22 / Chapter 3.2.2 --- 1-d Histogram --- p.23 / Chapter 3.2.3 --- 1-d Dense Region --- p.25 / Chapter 3.2.4 --- Signature Q --- p.26 / Chapter 3.3 --- The overall framework --- p.28 / Chapter 3.4 --- Major Steps --- p.30 / Chapter 3.4.1 --- Histogram Generation --- p.30 / Chapter 3.4.2 --- Adaptive discovery of dense regions --- p.31 / Chapter 3.4.3 --- Count the occurrences of signatures --- p.36 / Chapter 3.4.4 --- Find the most frequent signatures --- p.36 / Chapter 3.4.5 --- Refine the top 3m signatures --- p.37 / Chapter 3.5 --- Time and Space Complexity --- p.38 / Chapter 4 --- EPCH: An extension and generalization of EPC --- p.40 / Chapter 4.1 --- Motivation of the extension --- p.40 / Chapter 4.2 --- Distinguish clusters by their projections in different subspaces --- p.43 / Chapter 4.3 --- EPCH: a generalization of EPC by building histogram with higher dimensionality --- p.46 / Chapter 4.3.1 --- Multidimensional histograms construction and dense re- gions detection --- p.46 / Chapter 4.3.2 --- Compressing data objects to signatures --- p.47 / Chapter 4.3.3 --- Merging Similar Signature Entries --- p.49 / Chapter 4.3.4 --- Associating membership degree --- p.51 / Chapter 4.3.5 --- The choice of Dimensionality d of the Histogram --- p.52 / Chapter 4.4 --- Implementation of EPC2 --- p.53 / Chapter 4.5 --- Time and Space Complexity of EPCH --- p.54 / Chapter 5 --- Experimental Results --- p.56 / Chapter 5.1 --- Clustering Quality Measurement --- p.56 / Chapter 5.2 --- Synthetic Data Generation --- p.58 / Chapter 5.3 --- Experimental setup --- p.59 / Chapter 5.4 --- Comparison between EPC and PROCULS --- p.60 / Chapter 5.5 --- Comparison between EPCH and ORCLUS --- p.62 / Chapter 5.5.1 --- Dimensionality of the original space and the associated subspace --- p.65 / Chapter 5.5.2 --- Projection not parallel to original axes --- p.66 / Chapter 5.5.3 --- Data objects belong to more than one cluster under fuzzy clustering --- p.67 / Chapter 5.6 --- Scalability of EPC --- p.68 / Chapter 5.7 --- Scalability of EPC2 --- p.69 / Chapter 6 --- Conclusion --- p.71 / Chapter II --- Multiple Tables Association Rules Mining --- p.74 / Chapter 7 --- Introduction to Multiple Tables Association Rule Mining --- p.75 / Chapter 7.1 --- Problem Statement --- p.77 / Chapter 8 --- Related Work to Multiple Tables Association Rules Mining --- p.80 / Chapter 8.1 --- Aprori - A Bottom-up approach to generate candidate sets --- p.80 / Chapter 8.2 --- VIPER - Vertical Mining with various optimization techniques --- p.81 / Chapter 8.2.1 --- Vertical TID Representation and Mining --- p.82 / Chapter 8.2.2 --- FORC --- p.83 / Chapter 8.3 --- Frequent Itemset Counting across Multiple Tables --- p.84 / Chapter 9 --- The Proposed Method --- p.85 / Chapter 9.1 --- Notations --- p.85 / Chapter 9.2 --- Converting Dimension Tables to internal representation --- p.87 / Chapter 9.3 --- The idea of discovering frequent itemsets without joining --- p.89 / Chapter 9.4 --- Overall Steps --- p.91 / Chapter 9.5 --- Binding multiple Dimension Tables --- p.92 / Chapter 9.6 --- Prefix Tree for FT --- p.94 / Chapter 9.7 --- Maintaining frequent itemsets in FI-trees --- p.96 / Chapter 9.8 --- Frequency Counting --- p.99 / Chapter 10 --- Experiments --- p.102 / Chapter 10.1 --- Synthetic Data Generation --- p.102 / Chapter 10.2 --- Experimental Findings --- p.106 / Chapter 11 --- Conclusion and Future Works --- p.112 / Bibliography --- p.114

by Cai, Chun Hing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 109-114). / Abstract also in Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Main Categories in Data Mining --- p.1 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.3 --- Problem Definition --- p.4 / Chapter 1.4 --- Experimental Setup --- p.5 / Chapter 1.5 --- Outline of the thesis --- p.6 / Chapter 2 --- Literature Survey on Data Mining --- p.8 / Chapter 2.1 --- Statistical Approach --- p.8 / Chapter 2.1.1 --- Statistical Modeling --- p.9 / Chapter 2.1.2 --- Hypothesis testing --- p.10 / Chapter 2.1.3 --- Robustness and Outliers --- p.11 / Chapter 2.1.4 --- Sampling --- p.12 / Chapter 2.1.5 --- Correlation --- p.15 / Chapter 2.1.6 --- Quality Control --- p.16 / Chapter 2.2 --- Artificial Intelligence Approach --- p.18 / Chapter 2.2.1 --- Bayesian Network --- p.19 / Chapter 2.2.2 --- Decision Tree Approach --- p.20 / Chapter 2.2.3 --- Rough Set Approach --- p.21 / Chapter 2.3 --- Database-oriented Approach --- p.23 / Chapter 2.3.1 --- Characteristic and Classification Rules --- p.23 / Chapter 2.3.2 --- Association Rules --- p.24 / Chapter 3 --- Background --- p.27 / Chapter 3.1 --- Iterative Procedure: Apriori Gen --- p.27 / Chapter 3.1.1 --- Binary association rules --- p.27 / Chapter 3.1.2 --- Apriori Gen --- p.29 / Chapter 3.1.3 --- Closure Properties --- p.30 / Chapter 3.2 --- Introduction of Weights --- p.31 / Chapter 3.2.1 --- Motivation --- p.31 / Chapter 3.3 --- Summary --- p.32 / Chapter 4 --- Mining weighted binary association rules --- p.33 / Chapter 4.1 --- Introduction of binary weighted association rules --- p.33 / Chapter 4.2 --- Weighted Binary Association Rules --- p.34 / Chapter 4.2.1 --- Introduction --- p.34 / Chapter 4.2.2 --- Motivation behind weights and counts --- p.36 / Chapter 4.2.3 --- K-support bounds --- p.37 / Chapter 4.2.4 --- Algorithm for Mining Weighted Association Rules --- p.38 / Chapter 4.3 --- Mining Normalized Weighted association rules --- p.43 / Chapter 4.3.1 --- Another approach for normalized weighted case --- p.45 / Chapter 4.3.2 --- Algorithm for Mining Normalized Weighted Association Rules --- p.46 / Chapter 4.4 --- Performance Study --- p.49 / Chapter 4.4.1 --- Performance Evaluation on the Synthetic Database --- p.49 / Chapter 4.4.2 --- Performance Evaluation on the Real Database --- p.58 / Chapter 4.5 --- Discussion --- p.65 / Chapter 4.6 --- Summary --- p.66 / Chapter 5 --- Mining Fuzzy Weighted Association Rules --- p.67 / Chapter 5.1 --- Introduction to the Fuzzy Rules --- p.67 / Chapter 5.2 --- Weighted Fuzzy Association Rules --- p.69 / Chapter 5.2.1 --- Problem Definition --- p.69 / Chapter 5.2.2 --- Introduction of Weights --- p.71 / Chapter 5.2.3 --- K-bound --- p.73 / Chapter 5.2.4 --- Algorithm for Mining Fuzzy Association Rules for Weighted Items --- p.74 / Chapter 5.3 --- Performance Evaluation --- p.77 / Chapter 5.3.1 --- Performance of the algorithm --- p.77 / Chapter 5.3.2 --- Comparison of unweighted and weighted case --- p.79 / Chapter 5.4 --- Note on the implementation details --- p.81 / Chapter 5.5 --- Summary --- p.81 / Chapter 6 --- Mining weighted association rules with sampling --- p.83 / Chapter 6.1 --- Introduction --- p.83 / Chapter 6.2 --- Sampling Procedures --- p.84 / Chapter 6.2.1 --- Sampling technique --- p.84 / Chapter 6.2.2 --- Algorithm for Mining Weighted Association Rules with Sampling --- p.86 / Chapter 6.3 --- Performance Study --- p.88 / Chapter 6.4 --- Discussion --- p.91 / Chapter 6.5 --- Summary --- p.91 / Chapter 7 --- Database Maintenance with Quality Control method --- p.92 / Chapter 7.1 --- Introduction --- p.92 / Chapter 7.1.1 --- Motivation of using the quality control method --- p.93 / Chapter 7.2 --- Quality Control Method --- p.94 / Chapter 7.2.1 --- Motivation of using Mil. Std. 105D --- p.95 / Chapter 7.2.2 --- Military Standard 105D Procedure [12] --- p.95 / Chapter 7.3 --- Mapping the Database Maintenance to the Quality Control --- p.96 / Chapter 7.3.1 --- Algorithm for Database Maintenance --- p.98 / Chapter 7.4 --- Performance Evaluation --- p.102 / Chapter 7.5 --- Discussion --- p.104 / Chapter 7.6 --- Summary --- p.105 / Chapter 8 --- Conclusion and Future Work --- p.106 / Chapter 8.1 --- Summary of the Thesis --- p.106 / Chapter 8.2 --- Conclusions --- p.107 / Chapter 8.3 --- Future Work --- p.108 / Bibliography --- p.108 / Appendix --- p.115 / Chapter A --- Generating a random number --- p.115 / Chapter B --- Hypergeometric distribution --- p.116 / Chapter C --- Quality control tables --- p.117 / Chapter D --- Rules extracted from the database --- p.120

During the past two decades or so, graphical representations have been used increasingly for the examination, summarisation and communication of statistical data. Many graphical techniques exist for exploratory data analysis (ie. for deciding which model it is appropriate to fit to the data) and a number of graphical diagnostic techniques exist for checking the appropriateness of a fitted model. However, very few techniques exist for the representation of the fitted model itself. This thesis is concerned with the development of some new and existing graphical representation techniques for the communication and interpretation of fitted statistical models. The first part of this thesis takes the form of a general overview of the use in statistics of graphical representations for exploratory data analysis and diagnostic model checking. In relation to the concern of this thesis, particular consideration is given to the few graphical techniques which already exist for the representation of fitted models. A number of novel two-dimensional approaches are then proposed which go partway towards providing a graphical representation of the main effects and interaction terms for fitted models. This leads on to a description of conditional independence graphs, and consideration of the suitability of conditional independence graphs as a technique for the representation of fitted models. Conditional independence graphs are then developed further in accordance with the research aims. Since it becomes apparent that it is not possible to use any of the approaches taken m order to develop a simple two-dimensional pen-and-paper technique for the unambiguous graphical representation of all fitted statistical models, an interactive computer package based on the conditional independence graph approach is developed for the construction, communication and interpretation of graphical representations for fitted statistical models. This package, called the "Conditional Independence Graph Enhancer" (CIGE), does provide unambiguous graphical representations for all fitted statistical models considered.

Wang, Tianming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 59-62). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

" More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it. As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user's interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques. A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis. A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time. "