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研究生： 許大為
David - Alexandre
論文名稱： 使用向量空間模型改善之萬用演算法為基礎之文件分群
Meta-heuristic Based Document Clustering using Vector Space Model Modification
指導教授： 郭人介
Ren-Jieh Kuo
口試委員: 喻奉天
Vincent F. Yu
林希偉
Shi-Woei Lin
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 90
中文關鍵詞： Cluster analysis 、Document clustering 、Meta-heuristic 、Vector space model
外文關鍵詞： Cluster analysis, Document clustering, Meta-heuristic, Vector space model
相關次數： 點閱：322  下載：1 
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    • This study attempts to employ document clustering algorithm by using meta-heuristic algorithm and vector space model modification method to improve the performance of document clustering. The proposed vector space model modification in document clustering algorithm is performed in the similarity calculation. In order to optimize the usability, the vector space model modification is combined with meta-heuristic algorithm. The document clustering with vector space model modification tries to find the most significant part in each document vector space to calculate the highest similarity between two documents instead of calculate the similarity from whole document vector space. This proposed algorithm is compared with the ordinary method using four benchmark data sets, SMS Spam Detection, WebKB, Reuters-8, and Reuters-52. The simulation results indicate that document clustering algorithm with vector space model modification can improve the performances.

    This study attempts to employ document clustering algorithm by using meta-heuristic algorithm and vector space model modification method to improve the performance of document clustering. The proposed vector space model modification in document clustering algorithm is performed in the similarity calculation. In order to optimize the usability, the vector space model modification is combined with meta-heuristic algorithm. The document clustering with vector space model modification tries to find the most significant part in each document vector space to calculate the highest similarity between two documents instead of calculate the similarity from whole document vector space. This proposed algorithm is compared with the ordinary method using four benchmark data sets, SMS Spam Detection, WebKB, Reuters-8, and Reuters-52. The simulation results indicate that document clustering algorithm with vector space model modification can improve the performances.

    ABSTRACT i ACKNOWLEDGEMENT ii CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi Chapter 1 INTRODUCTION 1 1.1. Research Background 1 1.2. Research Objectives 2 1.3. Research Scopes and Constraints 3 1.4. Thesis Organization 3 Chapter 2 LITERATURE SURVEY 5 2.1. Text Document Clustering 5 2.1.1. Basic Concept 5 2.1.2. Data Representation 10 2.1.3. Data Preprocessing 11 2.1.4. Validation Methods 13 2.2. Text Document Clustering Methods 15 2.3. Meta-Heuristic Methods 17 2.3.1. Particle Swarm Optimization (PSO) Algorithm 18 2.3.2. Genetics Algorithm 19 2.4. Meta-heuristics in Text Document Clustering 20 Chapter 3 METHODOLOGY 23 3.1. Document Clustering with Vector Space Modification 26 3.2. Document Clustering with Meta-heuristic and Vector Space Modification 31 3.2.1. Particle Swarm Optimization based Modified Vector Space 31 3.2.2. Genetic Algorithm based Modified Vector Space 34 Chapter 4 COMPUTATIONAL RESULTS AND ANALYSIS 38 4.1. Parameters Setup 38 4.2. Computational Results 41 4.3. Statistical Test 45 4.4. Algorithm Convergence 49 Chapter 5 CONCLUSIONS AND FUTURE RESEARCH 55 5.1. Conclusion 55 5.2. Contributions 57 5.3. Future Research 58 REFERENCE 59 Appendix I COMPUTATIONAL RESULT 62 Appendix II STATISTICAL RESULT OF PROPOSED ALGORITHMS 78

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