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Data Mining in Dynamic Social Networks and Fuzzy Systems

Book Description

Many organizations, whether in the public or private sector, have begun to take advantage of the tools and techniques used for data mining. Utilizing data mining tools, these organizations are able to reveal the hidden and unknown information from available data. Data Mining in Dynamic Social Networks and Fuzzy Systems brings together research on the latest trends and patterns of data mining tools and techniques in dynamic social networks and fuzzy systems. With these improved modern techniques of data mining, this publication aims to provide insight and support to researchers and professionals concerned with the management of expertise, knowledge, information, and organizational development.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Foreword
  7. Preface
    1. SECTION 1: DATA MINING IN DYNAMIC SOCIAL NETWORKS
    2. SECTION 2: DATA MINING IN FUZZY SYSTEMS
  8. Acknowledgment
  9. Section 1: Data Mining in Dynamic Social Networks
    1. Chapter 1: Need for Dynamicity in Social Networking Sites
      1. ABSTRACT
      2. INTRODUCTION
      3. WHAT IS A SOCIAL MEDIA NETWORKING SITE (SMNS)?
      4. DYNAMICITY IN SOCIAL NETWORKING ENVIRONMENT
      5. APPLICATIONS OF SOCIAL MEDIA NETWORING SITES (SMNSS)
      6. WHO ARE USING SOCIAL MEDIA NETWORKING SITES (SMNSS)?
      7. SOCIAL MEDIA ASSOCIATED WITH NETWORKING
      8. FUNCTIONALITY AND DESIGN ISSUES OF SMNSS
      9. DESIGN AND IMPLEMENTATION OF SMNS
      10. EVOLUTION OF SOCIAL MEDIA NETWORKING SITE (SMNSS)
      11. PROBLEMS AND CHALLENGES ASSOCIATED WITH SOCIAL MEDIA AND NETWORKING
      12. PRIVACY ISSUES AND CONCERNS WITH SOCIAL MEDIA NETWORKING SITES
      13. BLOG PUBLISHING SERVICES (BLOGGER)
      14. DATA MINING OF SOCIAL MEDIA AND NETWORKING
      15. WHY DATA MINING BIG DATA IN SOCIAL NETWORKING?
      16. CONCLUSION AND FUTURE WORK
    2. Chapter 2: Data Preprocessing for Dynamic Social Network Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED RESEARCH
      4. RESEARCH METHODOLOGY
      5. SOCIAL NETWORK ANALYSIS
      6. IMPORTANCE OF DATA PREPROCESSING IN A SOCIAL NETWORK
      7. STEPS IN DATA PREPROCESSING
      8. HYPOTHETICAL CASE STUDY
      9. IMPLICATION/BENEFITS OF DATA PREPROCESSING IN A SOCIAL NETWORK
      10. LIMITATIONS OF THE STUDY
      11. FUTURE RESEARCH
      12. CONCLUSION
    3. Chapter 3: Emergent Data Mining Tools for Social Network Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. WEB-CONTENT EXTRACTION TECHNOLOGIES
      4. CONSIDERATIONS FOR DATA MINING OF ONLINE SOCIAL NETWORKS
      5. REVIEW OF EXISTING DATA-MINING TOOLS TO MINE ONLINE SOCIAL NETWORKS
      6. EXPERIMENTAL RESULTS: A CASE STUDY
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    4. Chapter 4: A Conceptual Framework for Social Network Data Security
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED RESEARCH AND MOTIVATION
      4. RESEARCH METHODOLOGY
      5. CONCEPTUAL FRAMEWORK FOR SOCIAL NETWORK DATA SECURITY
      6. EMPIRICAL CASE STUDY
      7. RESEARCH IMPLICATIONS
      8. ADVANTAGES OF THE FRAMEWORK
      9. LIMITATION OF THE FRAMEWORK
      10. CONCLUSION AND FUTURE WORK
    5. Chapter 5: Analyzing Twitter User-Generated Content Changes
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. THE TWICHI FRAMEWORK
      5. EXPERIMENTAL VALIDATION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
    6. Chapter 6: Applications of Data Mining in Dynamic Social Network Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION
    7. Chapter 7: Dynamic Social Network Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA MINING TECHNIQUES
      4. DYNAMIC SOCIAL NETWORK ANALYSIS
      5. APPLICATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION AND SUGGESTED READINGS
    8. Chapter 8: Data Mining Prospects in Mobile Social Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RELATED WORKS
      4. 3. MOBILE SOCIAL NETWORK MINING PROSPECTS
      5. 4. DATA MINING PROSPECTS AND ALGORITHMS
      6. 5. PERFORMANCE EVALUATION
      7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    9. Chapter 9: Semantic Integrating for Intelligent Cloud Data Mining Platform and Cloud Based Business Intelligence for Optimization of Mobile Social Networks
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND AND RELATED WORK DISCUSSION
      4. 3. PHILOSOPHICAL OVERVIEW OF SEMANTIC INTELLIGENT CLOUD BASED MOBILE SOCIAL NETWORK
      5. 4. SEMANTIC INTEGRATION OF INTELLIGENT CLOUD AGENTS FOR MOBILE SOCIAL NETWORKS
      6. 5. PROPOSED SEMANTIC INTELLIGENT CLOUD MODEL FOR OPTIMIZATION OF MOBILE SOCIAL NETWORKS
      7. 6. PATTERN AND PRINCIPLES OF MOBILE SOCIAL NETWORKS
      8. 7. PERFORMANCE DISCUSSION OF SEMANTIC INTELLIGENT CLOUD SYSTEMS
      9. CONCLUSION AND FUTURE RESEARCH WORK
    10. Chapter 10: Social Media Analytics
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND AND MOTIVATION
      4. PROCESS PARADIGM AND APPLICATIONS OF DATA MINING IN SOCIAL MEDIA
      5. DATA MINING TECHNIQUES
      6. NEXT GENERATION DATA MINING TECHNIQUES
      7. SOME RECENT APPLICATIONS
      8. CHALLENGES AND OPPORTUNITIES
      9. DISCUSSIONS
      10. FUTURE DIRECTIONS
      11. IMPLICATION OF THE WORK AND FUTURE RESEARCH DIRECTIONS IN ANALYSIS OF DATA FROM SOCIAL MEDIA
      12. CONCLUSION
  10. Section 2: Data Mining in Fuzzy Systems
    1. Chapter 11: Critical Parameters for Fuzzy Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SETTING UP THE CHAPTER
      5. CRITICAL PARAMETERS FOR APPLYING FUZZY DATA MINING
      6. DISCUSSION AND RESEARCH IMPLICATIONS
      7. LIMITATIONS OF THE STUDY
      8. CONCLUSION AND FUTURE RESEARCH
    2. Chapter 12: New Trends in Fuzzy Clustering
      1. ABSTRACT
      2. INTRODUCTION
      3. UNCERTAINTY, IMPRECISION AND FUZZINESS
      4. SETS AND SYSTEM MODELING
      5. LOGICAL CONCEPTS
      6. CLUSTER METHODOLOGIES
      7. FORMAL CLUSTER METHODS
      8. INNOVATIVE FUZZY TREND CLUSTERING
      9. EARTHQUAKE VISUAL DATA AND FUZZY CLUSTER ASSESSMENT
      10. FUTURE DIRECTIONS
      11. CONCLUSION
    3. Chapter 13: Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. TRAJECTORY DATASET
      5. FUZZY LANE CHANGING MODELS
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
    4. Chapter 14: User Segmentation Based on Twitter Data Using Fuzzy Clustering
      1. ABSTRACT
      2. INTRODUCTION
      3. SOCIAL NETWORKS AND BUSINESS APPLICATIONS
      4. METHODOLOGY
      5. NUMERICAL APPLICATION: USER SEGMENTATION BASED ON TWITTER DATA
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
    5. Chapter 15: Defining the Factors that Effect User Interest on Social Network News Feeds via Fuzzy Association Rule Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. METHODOLOGY
      5. EXPERIMENTAL EVALUATION OF THE PROPOSED APPROACH: SPORTS PORTAL NEWS FEEDS
      6. CONCLUSION
  11. Compilation of References
  12. About the Contributors