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
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324004 |
| Date | January 2002 |
| Contributors | Ng, Ka Ka., 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, xv, 120 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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