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Student Number 975202072
Author Yi-ling Kuo(̲)
Author's Email Address masagi0723@gmail.com
Statistics This thesis had been viewed 688 times. Download 342 times.
Department Computer Science and Information Engineering
Year 2009
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title Hierarchical Role Classification based on Social Behavior Analysis
Date of Defense 2010-07-20
Page Count 44
Keyword
  • Community Detection
  • Document Classification
  • Fuzzy Set Theory
  • Social Network Analysis
  • Abstract Social network analysis is a methodology to collect, analyze, and display the community relationship under different scenarios, it utilizes varied techniques to measure the social information, user-generated content, and social interaction. The last few years have seen a great deal of work on social network analysis. Community detection and discovery particularly is the most popular filed, and it can find the hidden communities to further analysis, such as community recommendation. However, role identification is a difficult job for many social network applications. One of the difficulties is to maintain and utilize large amount of distinct roles. And we found that there are few studies of any kind have examined the influence of using concept hierarchy to social network abstraction. In this paper, we attempt to adapt fuzzy classification method and construct a hierarchy for role classification, in other words, we want to design a role classification methodology based on the documents which users are interested in, and attempts to form the role hierarchy automatically then analyzes it. We believe this approach can encourage the utilization of social roles by considering their identifiable features at different levels.
    Table of Content Chinese Abstracti
    English Abstracii
    Acknowledgmentiii
    Table of Contentsiv
    List of Figuresvi
    List of Tablesvii
    Chapter 1Introduction1
    1-1Motivation1
    1-2Contribution4
    Chapter 2Related Work6
    Chapter 3System Architecture11
    Chapter 4Methodology14
    4-1Deep community15
    A.Social behavior classification based on fuzzy classification model16
    B.Concept hierarchy of category18
    C.Abstract community20
    Chapter 5Experiment23
    5-1Experimental data sets23
    5-2Cosine similarity24
    5-3SVM25
    5-4Results and discussions25
    Chapter 6Conclusion34
    Reference35
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    [20] http://digg.com The Latest News Headlines, Videos and Images.
    Advisor
  • Meng-feng Tsai(sp)
  • Files
  • 975202072.pdf
  • approve in 2 years
    Date of Submission 2010-08-27

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