by Lau Tak Kan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 100-108). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Problem Defined --- p.5 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Content-based Retrieval Multimedia Database Background and Indexing Problem --- p.8 / Chapter 2.1 --- Feature Extraction --- p.8 / Chapter 2.2 --- Nearest-neighbor Search --- p.10 / Chapter 2.3 --- Content-based Indexing Methods --- p.15 / Chapter 2.4 --- Indexing Problem --- p.22 / Chapter 3 --- Data Clustering Methods for Indexing --- p.25 / Chapter 3.1 --- Proposed Solution to Indexing Problem --- p.25 / Chapter 3.2 --- Brief Description of Several Clustering Methods --- p.26 / Chapter 3.2.1 --- K-means --- p.26 / Chapter 3.2.2 --- Competitive Learning (CL) --- p.27 / Chapter 3.2.3 --- Rival Penalized Competitive Learning (RPCL) --- p.29 / Chapter 3.2.4 --- General Hierarchical Clustering Methods --- p.31 / Chapter 3.3 --- Why RPCL? --- p.32 / Chapter 4 --- Non-hierarchical RPCL Indexing --- p.33 / Chapter 4.1 --- The Non-hierarchical Approach --- p.33 / Chapter 4.2 --- Performance Experiments --- p.34 / Chapter 4.2.1 --- Experimental Setup --- p.35 / Chapter 4.2.2 --- Experiment 1: Test for Recall and Precision Performance --- p.38 / Chapter 4.2.3 --- Experiment 2: Test for Different Sizes of Input Data Sets --- p.45 / Chapter 4.2.4 --- Experiment 3: Test for Different Numbers of Dimensions --- p.49 / Chapter 4.2.5 --- Experiment 4: Compare with Actual Nearest-neighbor Results --- p.53 / Chapter 4.3 --- Chapter Summary --- p.55 / Chapter 5 --- Hierarchical RPCL Indexing --- p.56 / Chapter 5.1 --- The Hierarchical Approach --- p.56 / Chapter 5.2 --- The Hierarchical RPCL Binary Tree (RPCL-b-tree) --- p.58 / Chapter 5.3 --- Insertion --- p.61 / Chapter 5.4 --- Deletion --- p.63 / Chapter 5.5 --- Searching --- p.63 / Chapter 5.6 --- Experiments --- p.69 / Chapter 5.6.1 --- Experimental Setup --- p.69 / Chapter 5.6.2 --- Experiment 5: Test for Different Node Sizes --- p.72 / Chapter 5.6.3 --- Experiment 6: Test for Different Sizes of Data Sets --- p.75 / Chapter 5.6.4 --- Experiment 7: Test for Different Data Distributions --- p.78 / Chapter 5.6.5 --- Experiment 8: Test for Different Numbers of Dimensions --- p.80 / Chapter 5.6.6 --- Experiment 9: Test for Different Numbers of Database Ob- jects Retrieved --- p.83 / Chapter 5.6.7 --- Experiment 10: Test with VP-tree --- p.86 / Chapter 5.7 --- Discussion --- p.90 / Chapter 5.8 --- A Relationship Formula --- p.93 / Chapter 5.9 --- Chapter Summary --- p.96 / Chapter 6 --- Conclusion --- p.97 / Chapter 6.1 --- Future Works --- p.97 / Chapter 6.2 --- Conclusion --- p.98 / Bibliography --- p.100

Wong Shu Yan. / On t.p. "n" is subscript. / Thesis submitted in: January 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 112-115). / Abstracts in English and Chinese. / TITLE PAGE --- p.i / THESIS EXAMINATION COMMITTEE --- p.ii / ABSTRACT (ENGLISH) --- p.iii / (CHINESE) --- p.v / ACKNOWLEDGEMENTS --- p.vii / TABLE OF CONTENTS --- p.viii / LIST OF FIGURES --- p.xi / LIST OF TABLES --- p.xiii / Chapter CHAPTER ONE --- Introduction / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Solvation of clusters --- p.2 / Chapter 1.3 --- Reaction of a sodium atom with water --- p.3 / Chapter 1.4 --- Reaction of a sodium cation with methanol --- p.8 / Chapter 1.5 --- Computational Method --- p.12 / Chapter 1.5.1 --- Born-Oppenheimer (BO) Approximation --- p.12 / Chapter 1.5.2 --- Self-Consistent Fields (SCF) ´ؤ Hartree-Fock (HF) --- p.14 / Chapter 1.5.2.1 --- Moller-Plesset (MP) Perturbation Theory --- p.15 / Chapter 1.5.2.2 --- Ab Initio Molecular Orbital (MO) Calculation --- p.16 / Chapter 1.5.2.3 --- Basis Set Superposition Errors --- p.17 / Chapter 1.5.3 --- Density Functional Theory (DFT) --- p.18 / Chapter 1.5.3.1 --- Generalized-Gradient Approximation (GGA) --- p.20 / Chapter 1.5.3.2 --- Plane-wave Basis Set --- p.21 / Chapter 1.5.3.3 --- Pseudopotential Approximation --- p.21 / Chapter 1.5.3.4 --- Ab Initio Molecular Dynamics (MD) Calculation --- p.23 / Chapter CHAPTER TWO --- Reaction Mechanism of the Hydrogen Elimination Reaction of Na(H20)n clusters for n = 1 - 6 / Chapter 2.1 --- Introduction --- p.25 / Chapter 2.2 --- Computation details --- p.26 / Chapter 2.3 --- Optimized Structure of Na(H20)n and H.. .Na0H(H20)n-1 --- p.27 / Chapter 2.3.1 --- Solvation structures with n = 1-3 --- p.27 / Chapter 2.3.2 --- Solvation structures with n= 4-6 --- p.34 / Chapter 2.3.3 --- Relative energy of isomers --- p.40 / Chapter 2.3.4 --- Energy barrier of hydrogen elimination reaction --- p.42 / Chapter 2.3.5 --- Natural population analysis --- p.42 / Chapter 2.4 --- "Reaction energy for hydrogen loss in Na(H20)n, n = 1 -6" --- p.46 / Chapter 2.5 --- Ionization potential energy --- p.47 / Chapter 2.6 --- Summary --- p.50 / Chapter CHAPTER THREE --- Reaction Mechanism of the Ether Elimination Reaction of Na+(CH3OH)n cluster ions / Chapter 3.1 --- Introduction --- p.52 / Chapter 3.2 --- Computational details --- p.53 / Chapter 3.3 --- Optimized Structure for Na+(CH3OH)n (n = 1) --- p.55 / Chapter 3.4 --- Optimized Structure forNa+(CH3OH)n (n = 2-5) --- p.59 / Chapter 3.4.1 --- Na+(CH3OH)2 --- p.59 / Chapter 3.4.2 --- Na+(CH3OH)3 --- p.67 / Chapter 3.4.3 --- Na+(CH3OH)n(n = 4 and 5) --- p.75 / Chapter 3.5 --- Mechanism of ether elimination reaction --- p.79 / Chapter 3.6 --- Ab initio molecular dynamics study on Na+(CH3OH)n (n =6 and 8) --- p.85 / Chapter 3.6.1 --- Solvation dynamics for Na+(CH3OH)6 --- p.85 / Chapter 3.6.1.1 --- Dynamical structural for Na+(CH3OH)6 --- p.86 / Chapter 3.6.1.2 --- "Optimized Structures for Na+(CH3OH)n, n =6" --- p.95 / Chapter 3.6.2 --- Solvation dynamics for Na+(CH3OH)8 --- p.98 / Chapter 3.6.2.1 --- Dynamical structural for Na+(CH3OH)8 --- p.99 / Chapter 3.6.2.2 --- "Optimized Structures for Na+(CH3OH)n, n =8" --- p.106 / Chapter 3.7 --- Summary --- p.109 / REFERENCES --- p.112

by Ka-Kit Tse. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references. / Acknowledgments --- p.i / List of tables --- p.v / Chapter Chapter 1: --- Overall Review --- p.1 / Chapter Chapter 2: --- Essay one - A Mathematical Programming Approach to Clusterwise Regression Model and its Extensions / Chapter 2.0. --- Abstract --- p.5 / Chapter 2.1. --- Introduction --- p.6 / Chapter 2.2. --- A Mathematical Programming Formulation of the Clusterwise Regression Model --- p.10 / Chapter 2.2.1. --- The Generalized Clusterwise Regression Model --- p.10 / Chapter 2.2.2. --- "Clusterwise Regression Model (Spath, 1979)" --- p.14 / Chapter 2.2.3. --- A Nonparametric Clusterwise Regression Model --- p.15 / Chapter 2.2.4. --- A Mixture Approach to Clusterwise Regression Model --- p.16 / Chapter 2.2.5. --- An Illustrative Application --- p.19 / Chapter 2.3. --- Mathematical Programming Formulation of the Clusterwise Discriminant Analysis --- p.21 / Chapter 2.4. --- Conclusion --- p.25 / Chapter 2.5. --- Appendix --- p.28 / Chapter 2.6. --- References --- p.32 / Chapter 2.7. --- Tables --- p.35 / Chapter Chapter 3: --- Essay two - A Mathematical Programming Approach to Clusterwise Rank Order Logit Model / Chapter 3.0. --- Abstract --- p.40 / Chapter 3.1. --- Introduction --- p.41 / Chapter 3.2. --- Clusterwise Rank Order Logit Model --- p.42 / Chapter 3.3. --- Numerical Illustration --- p.46 / Chapter 3.4. --- Conclustion --- p.48 / Chapter 3.5. --- References --- p.50 / Chapter 3.6. --- Tables --- p.52 / Chapter Chapter 4: --- Essay three - A Mathematical Programming Approach to Metric Unidimensional Scaling / Chapter 4.0. --- Abstract --- p.53 / Chapter 4.1. --- Introduction --- p.54 / Chapter 4.2. --- Nonlinear Programming Formulation --- p.56 / Chapter 4.3. --- Numerical Examples --- p.60 / Chapter 4.4. --- Possible Extensions --- p.61 / Chapter 4.5. --- Conclusion and Extensions --- p.63 / Chapter 4.6. --- References --- p.64 / Chapter 4.7. --- Tables --- p.66 / Chapter Chapter 5: --- Research Project in Progress / Chapter 5.1. --- Project 1 -- An Integrated Approach to Taste Test Experiment Within the Prospect Theory Framework --- p.68 / Chapter 5.1.1. --- Experiment Procedure --- p.68 / Chapter 5.1.2. --- Experimental Result --- p.72 / Chapter 5.2. --- Project 2 -- An Integrated Approach to Multi- Dimensional Scaling Problem --- p.75 / Chapter 5.2.1. --- Introduction --- p.75 / Chapter 5.2.2. --- Experiment Procedure --- p.76 / Chapter 5.2.3. --- Questionnaire --- p.78 / Chapter 5.2.4. --- Experimental Result --- p.78

Lui Ying Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 63-65). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Introduction to bootstrap and efficiency bootstrap simulation --- p.5 / Chapter 2.1 --- Background of bootstrap --- p.5 / Chapter 2.2 --- Basic idea of bootstrap --- p.7 / Chapter 2.3 --- Variance reduction methods --- p.10 / Chapter 2.3.1 --- Control variates --- p.10 / Chapter 2.3.2 --- Common random numbers --- p.12 / Chapter 2.3.3 --- Antithetic variates --- p.14 / Chapter 2.3.4 --- Importance Sampling --- p.15 / Chapter 2.4 --- Efficient bootstrap simulation --- p.17 / Chapter 2.4.1 --- Linear approximation --- p.18 / Chapter 2.4.2 --- Centring method --- p.19 / Chapter 2.4.3 --- Balanced resampling --- p.20 / Chapter 2.4.4 --- Antithetic resampling --- p.21 / Chapter 3 --- Methodology --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- Cluster analysis --- p.24 / Chapter 3.3 --- Regression estimator and mixture experiment --- p.25 / Chapter 3.4 --- Estimate of standard error and bias --- p.30 / Chapter 4 --- Simulation study --- p.45 / Chapter 4.1 --- Introduction --- p.45 / Chapter 4.2 --- Ratio estimation --- p.46 / Chapter 4.3 --- Time series problem --- p.50 / Chapter 4.4 --- Regression problem --- p.54 / Chapter 5 --- Conclusion and discussion --- p.60 / Reference --- p.63

No description available.

Chan, Hon Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 57-59). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Cluster Analysis in Time Series --- p.1 / Chapter 1.2 --- Dissimilarity Measure --- p.2 / Chapter 1.2.1 --- Euclidean Distance --- p.3 / Chapter 1.2.2 --- Pearson's Correlation Coefficient --- p.3 / Chapter 1.2.3 --- Other Measure --- p.4 / Chapter 1.3 --- Summary --- p.5 / Chapter 2 --- Algorithm and Methodology --- p.8 / Chapter 2.1 --- Algorithm and Methodology --- p.8 / Chapter 2.2 --- Illustrative Examples --- p.14 / Chapter 3 --- Simulation Study --- p.20 / Chapter 3.1 --- Simulation Plan --- p.20 / Chapter 3.2 --- Measure of Performance --- p.24 / Chapter 3.3 --- Simulation Results --- p.27 / Chapter 3.4 --- Results of k-means Clustering --- p.33 / Chapter 4 --- Application on Gene Expression --- p.37 / Chapter 4.1 --- Dataset --- p.37 / Chapter 4.2 --- Parameter Settings --- p.38 / Chapter 4.3 --- Results --- p.38 / Chapter 5 --- Conclusion and Further Research --- p.55

Bu, Yingyi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 89-94). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Anomaly Monitoring --- p.1 / Chapter 1.2 --- Privacy Preservation --- p.5 / Chapter 1.2.1 --- Motivation --- p.7 / Chapter 1.2.2 --- Contribution --- p.12 / Chapter 2 --- Anomaly Monitoring --- p.16 / Chapter 2.1 --- Problem Statement --- p.16 / Chapter 2.2 --- A Preliminary Solution: Simple Pruning --- p.19 / Chapter 2.3 --- Efficient Monitoring by Local Clusters --- p.21 / Chapter 2.3.1 --- Incremental Local Clustering --- p.22 / Chapter 2.3.2 --- Batch Monitoring by Cluster Join --- p.24 / Chapter 2.3.3 --- Cost Analysis and Optimization --- p.28 / Chapter 2.4 --- Piecewise Index and Query Reschedule --- p.31 / Chapter 2.4.1 --- Piecewise VP-trees --- p.32 / Chapter 2.4.2 --- Candidate Rescheduling --- p.35 / Chapter 2.4.3 --- Cost Analysis --- p.36 / Chapter 2.5 --- Upper Bound Lemma: For Dynamic Time Warping Distance --- p.37 / Chapter 2.6 --- Experimental Evaluations --- p.39 / Chapter 2.6.1 --- Effectiveness --- p.40 / Chapter 2.6.2 --- Efficiency --- p.46 / Chapter 2.7 --- Related Work --- p.49 / Chapter 3 --- Privacy Preservation --- p.52 / Chapter 3.1 --- Problem Definition --- p.52 / Chapter 3.2 --- HD-Composition --- p.58 / Chapter 3.2.1 --- Role-based Partition --- p.59 / Chapter 3.2.2 --- Cohort-based Partition --- p.61 / Chapter 3.2.3 --- Privacy Guarantee --- p.70 / Chapter 3.2.4 --- Refinement of HD-composition --- p.75 / Chapter 3.2.5 --- Anonymization Algorithm --- p.76 / Chapter 3.3 --- Experiments --- p.77 / Chapter 3.3.1 --- Failures of Conventional Generalizations --- p.78 / Chapter 3.3.2 --- Evaluations of HD-Composition --- p.79 / Chapter 3.4 --- Related Work --- p.85 / Chapter 4 --- Conclusions --- p.87 / Bibliography --- p.89

Ho, Siu Tung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 43-44). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 2 --- Methodology and Algorithm --- p.6 / Chapter 2.1 --- Testing one. homogeneous cluster --- p.8 / Chapter 3 --- Simulation Study --- p.17 / Chapter 3.1 --- Simulation Plan --- p.19 / Chapter 3.1.1 --- One single cluster --- p.19 / Chapter 3.1.2 --- Two separated clusters --- p.20 / Chapter 3.2 --- Measure of Performance --- p.26 / Chapter 3.3 --- Simulation Results --- p.27 / Chapter 3.3.1 --- One single cluster --- p.27 / Chapter 3.3.2 --- Two separated clusters --- p.30 / Chapter 4 --- Conclusion and further research --- p.36 / Chapter 4.1 --- Constructing Data depth --- p.38 / Bibliography --- p.43

This paper gives a description of three well known clustering methods, and discusses the advantages and disadvantages of each. Then, the results of these three clustering methods are compared through examining them on a specific set of data.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.