by Li Wai Kit. / Bibliography: leaves 170-178 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

So, King-yan Oldfield. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Evolution of Communication Technologies --- p.1 / Chapter 1.2 --- The Internet --- p.2 / Chapter 1.3 --- The Distributed Virtual Environments --- p.2 / Chapter 1.3.1 --- Features of DVE --- p.3 / Chapter 1.3.2 --- Current and Potential Applications --- p.4 / Chapter 1.3.3 --- The Challenges --- p.5 / Chapter 1.4 --- Our Contributions --- p.6 / Chapter 2 --- System Architecture --- p.7 / Chapter 2.1 --- The SSDVE and MSDVE Architectures --- p.7 / Chapter 2.2 --- Issues in the MSDVE Architecture --- p.8 / Chapter 2.2.1 --- On the Server Side --- p.8 / Chapter 2.2.2 --- On the Client Side --- p.8 / Chapter 3 --- Balancing Work Load and Reducing Inter-server Communication --- p.10 / Chapter 3.1 --- Problem Formulation --- p.10 / Chapter 3.1.1 --- The Area of Interest --- p.11 / Chapter 3.1.2 --- The DVE Cells --- p.11 / Chapter 3.1.3 --- Expected Number of Avatars --- p.12 / Chapter 3.1.4 --- Cost Metrics in Different Types of Network --- p.13 / Chapter 3.1.5 --- Problem Definition --- p.14 / Chapter 3.1.6 --- Complexity --- p.18 / Chapter 3.2 --- Partitioning Algorithms --- p.19 / Chapter 3.2.1 --- A Simplified Case --- p.19 / Chapter 3.2.2 --- The General Case --- p.19 / Chapter 3.3 --- Experiments --- p.22 / Chapter 4 --- Communication Sub-graph --- p.31 / Chapter 4.1 --- Problem Formulation --- p.31 / Chapter 4.1.1 --- Optimization Metrics --- p.32 / Chapter 4.1.2 --- Design Considerations --- p.32 / Chapter 4.2 --- Communication Sub-graph Construction Algorithms --- p.34 / Chapter 4.2.1 --- The Minimum Diameter Sub-graph (MDS) --- p.34 / Chapter 4.2.2 --- The Core-based Tree (CBT) --- p.37 / Chapter 4.2.3 --- The Minimum Spanning Tree (MST) --- p.40 / Chapter 5 --- Synchronization --- p.42 / Chapter 5.1 --- Synchronization in a DVE System --- p.43 / Chapter 5.2 --- System Model --- p.46 / Chapter 5.2.1 --- Problem Definition --- p.47 / Chapter 5.2.2 --- The Markov Chain Model --- p.47 / Chapter 5.2.3 --- Deciding the Threshold Φ --- p.49 / Chapter 5.3 --- Optimal Synchronizing Interval --- p.50 / Chapter 5.3.1 --- "An ""on-average"" Guarantee" --- p.50 / Chapter 5.3.2 --- A Stochastic Guarantee --- p.52 / Chapter 5.3.3 --- Finding p with T and Φ --- p.52 / Chapter 5.3.4 --- Searching for r*p --- p.54 / Chapter 5.4 --- Experiments --- p.55 / Chapter 5.4.1 --- Simulation Results --- p.55 / Chapter 5.4.2 --- Theoretical Results --- p.58 / Chapter 6 --- Related Work --- p.63 / Chapter 6.1 --- Load Balancing on DVE --- p.63 / Chapter 6.2 --- Object State Synchronization Techniques --- p.63 / Chapter 6.3 --- Group Communication and Multicasting --- p.64 / Chapter 6.4 --- DVE System Development Toolkits --- p.64 / Chapter 6.5 --- Example DVE Systems --- p.65 / Chapter 7 --- Conclusion --- p.66 / Chapter 7.1 --- A Vision to the Future --- p.66 / Chapter 7.2 --- Conclusion --- p.66 / Bibliography --- p.68

Chan Ming-fei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 63-66). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Challenging Issues --- p.2 / Chapter 1.2 --- Previous Work --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.5 / Chapter 2 --- Distributed Virtual Environment --- p.6 / Chapter 2.1 --- Possible Architectures --- p.6 / Chapter 2.2 --- Representations of Clients as Avatars --- p.7 / Chapter 2.3 --- Dynamic Membership --- p.9 / Chapter 3 --- Bandwidth and Computation Reduction Techniques --- p.11 / Chapter 3.1 --- Network Communication --- p.12 / Chapter 3.2 --- Dead Reckoning --- p.13 / Chapter 3.3 --- Message Aggregation --- p.15 / Chapter 3.3.1 --- Network-Based Aggregation --- p.15 / Chapter 3.3.2 --- Organization-Based Aggregations --- p.16 / Chapter 3.3.3 --- Grid-Based Aggregations --- p.16 / Chapter 3.4 --- Relevance Filtering --- p.17 / Chapter 3.4.1 --- Entity-Based Filtering --- p.17 / Chapter 3.4.2 --- Grid-Based Filtering --- p.19 / Chapter 3.5 --- Quiescent Entities --- p.20 / Chapter 3.6 --- Spatial Partitioning --- p.21 / Chapter 3.6.1 --- Necessity of Spatial Partitioning --- p.22 / Chapter 3.6.2 --- Binary Space Partitioning Tree --- p.23 / Chapter 3.6.3 --- BSP Tree Construction --- p.23 / Chapter 4 --- Partitioning Algorithm --- p.25 / Chapter 4.1 --- Problem Formulation --- p.25 / Chapter 4.2 --- Exhaustive Partition (EP) Algorithm --- p.28 / Chapter 4.3 --- Partitioning Algorithm --- p.29 / Chapter 4.3.1 --- Recursive Bisection Partition (RBP) Algorithm --- p.30 / Chapter 4.3.2 --- Layering Partitioning (LP) Algorithm --- p.32 / Chapter 4.3.3 --- Communication Refinement Partitioning (CRP) Algorithm --- p.38 / Chapter 4.4 --- Parallel Approach --- p.42 / Chapter 4.5 --- Further Observation --- p.43 / Chapter 5 --- Experiments --- p.44 / Chapter 5.1 --- Experiment 1: Small Virtual World --- p.45 / Chapter 5.2 --- Experiment 2: Large Virtual World --- p.46 / Chapter 5.3 --- Experiment 3: Moving of Avatars --- p.47 / Chapter 5.4 --- Experiment 4: Dynamic Joining and Leaving --- p.48 / Chapter 5.5 --- Experiment 5: Parallel Approach --- p.49 / Chapter 6 --- Implementation Considerations --- p.55 / Chapter 6.1 --- Different Environments --- p.55 / Chapter 6.2 --- Platform --- p.56 / Chapter 6.3 --- Lessons learned --- p.57 / Chapter 7 --- Conclusion --- p.59 / A Simplex Method --- p.60 / Bibliography --- p.63

by Chan Wai To. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 117-121). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Clustering --- p.3 / Chapter 1.2 --- Mobile Agent --- p.4 / Chapter 1.3 --- Contribution --- p.4 / Chapter 1.4 --- Outline of this Thesis --- p.5 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Clustering --- p.6 / Chapter 2.1.1 --- K-Means Clustering --- p.6 / Chapter 2.1.2 --- A more efficient K-Means Clustering Algorithm --- p.3 / Chapter 2.1.3 --- K-Medoids Clustering Algorithms --- p.8 / Chapter 2.1.4 --- Linkage-based Methods --- p.11 / Chapter 2.1.5 --- BIRCH --- p.13 / Chapter 2.1.6 --- DBSCAN --- p.14 / Chapter 2.1.7 --- Other Clustering Algorithm --- p.17 / Chapter 2.2 --- Parallel Clustering and Distributed Clustering --- p.17 / Chapter 2.2.1 --- A Fast Parallel Clustering Algorithm for Large Spatial Databases --- p.17 / Chapter 2.3 --- Distributed Data Mining --- p.18 / Chapter 2.3.1 --- A Distributed Clustering Algorithm --- p.18 / Chapter 2.3.2 --- Efficient Mining of Association Rules in Distributed Databases --- p.19 / Chapter 2.4 --- Information Retrieval and Document Clustering --- p.20 / Chapter 2.4.1 --- Document and Document Set Representation --- p.20 / Chapter 2.4.2 --- TFIDF --- p.20 / Chapter 2.4.3 --- Similarity --- p.21 / Chapter 2.4.4 --- Partitional Document Clustering --- p.22 / Chapter 2.4.5 --- Hierarchical Document Clustering --- p.22 / Chapter 2.4.6 --- Document Clustering Application --- p.23 / Chapter 3 --- Distributed Clustering --- p.24 / Chapter 3.1 --- Problem Description --- p.24 / Chapter 3.2 --- Distributed k-Means Clustering Algorithm --- p.25 / Chapter 3.2.1 --- Initialization --- p.25 / Chapter 3.2.2 --- weighted k-Means procedure --- p.26 / Chapter 3.2.3 --- Refinement --- p.27 / Chapter 3.2.4 --- Example --- p.31 / Chapter 3.2.5 --- Communication Cost --- p.34 / Chapter 3.3 --- Grid k-Mean --- p.34 / Chapter 3.3.1 --- Runtime Splitting --- p.36 / Chapter 3.3.2 --- Initial Clusters --- p.38 / Chapter 3.3.3 --- Refinement --- p.38 / Chapter 3.3.4 --- Overall Algorithm --- p.39 / Chapter 3.3.5 --- Efficiency in Decomposition --- p.42 / Chapter 3.3.6 --- Example --- p.42 / Chapter 3.3.7 --- Comparison with previous k-Means method --- p.43 / Chapter 3.3.8 --- Communication Cost --- p.44 / Chapter 3.4 --- Experiment --- p.44 / Chapter 3.4.1 --- Performance --- p.46 / Chapter 3.4.2 --- Communication Cost --- p.47 / Chapter 3.4.3 --- Quality of Clustering --- p.49 / Chapter 3.4.4 --- Clustering in High Dimension --- p.49 / Chapter 3.4.5 --- Other Data Distributions --- p.52 / Chapter 4 --- Distributed DBSCAN --- p.54 / Chapter 4.1 --- Representative points of local candidate clusters --- p.55 / Chapter 4.2 --- Verification and Cluster Merging --- p.57 / Chapter 4.2.1 --- Clustering Result Quality --- p.59 / Chapter 4.3 --- Experiment --- p.62 / Chapter 5 --- Document Clustering --- p.72 / Chapter 5.1 --- Initialization --- p.73 / Chapter 5.2 --- Refinement --- p.76 / Chapter 5.3 --- Stopping criteria --- p.77 / Chapter 5.4 --- Message --- p.77 / Chapter 5.5 --- Algorithm --- p.78 / Chapter 5.6 --- Experiment --- p.82 / Chapter 5.6.1 --- Data Source and Experimental Setup --- p.82 / Chapter 5.6.2 --- Data Size --- p.34 / Chapter 5.6.3 --- Evaluation Metrics --- p.85 / Chapter 5.6.4 --- Experimental Result --- p.85 / Chapter 5.6.5 --- Comparison to Other Algorithms --- p.94 / Chapter 5.6.6 --- Conclusion --- p.94 / Chapter 5.7 --- Future Work --- p.95 / Chapter 6 --- Agent and Implementation Details --- p.96 / Chapter 6.1 --- Agent Introduction --- p.96 / Chapter 6.1.1 --- Reason to use Mobile Agent --- p.97 / Chapter 6.1.2 --- Grasshopper Overview --- p.97 / Chapter 6.1.3 --- Agent Scenario --- p.98 / Chapter 6.1.4 --- Another Agent Scenario --- p.99 / Chapter 6.2 --- Implementation Details --- p.100 / Chapter 6.2.1 --- Distributed k-Means --- p.100 / Chapter 6.2.2 --- Grid k-Means --- p.104 / Chapter 6.2.3 --- Distributed DBSCAN --- p.109 / Chapter 6.2.4 --- Distributed Document Clustering --- p.112 / Chapter 7 --- Conclusion

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