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
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323377 |
| Date | January 2001 |
| Contributors | Chan, Wai To., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xvi, 121 leaves : ill. ; 30 cm. |
| Rights | Use 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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