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Ethical Data Mining Applications for Socio-Economic Development

Book Description

Organizations that utilize data mining techniques can amass valuable information on clients’ habits and preferences, behavior patterns, purchase patterns, sales patterns, and stock forecasts. Ethical Data Mining Applications for Socio-Economic Development provides an overview of data mining techniques under an ethical lens, investigating developments in research and best practices, while evaluating experimental cases to identify potential ethical dilemmas in the information and communications technology sector. The cases and research in this book will benefit scientists, researchers, and practitioners working in the field of data mining, data warehousing, and database management to ensure that data obtained through web-based investigations is properly handled at all organizational levels. This book is part of the Advances in Data Mining and Database Management series collection.

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
  6. Preface
    1. PREAMBLE
    2. AIMS AND OBJECTIVES
    3. ORGANIZATION OF THE BOOK
    4. CONCLUSION
  7. Acknowledgment
  8. Section 1: Theoretical and Conceptual Approaches
    1. Chapter 1: Ethical Issues of ‘Morality Mining’
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MORALITY MINING
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    2. Chapter 2: Scope and Limitations in Social Science Research
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS
      5. SOLUTIONS AND RECOMMENDATIONS
      6. CONCLUSION
    3. Chapter 3: Reuse of Excess Research Data for New Researches
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ETHICAL QUESTION
      5. SOLUTIONS AND RECOMMENDATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
    4. Chapter 4: Data Mining Techniques to Improve Early Warning Systems across the Bay of Bengal
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ISSUES, CONTROVERSIES, AND PROBLEMS
      5. SOLUTIONS AND RECOMMENDATIONS
      6. FUTURE ASPECTS
      7. CONCLUSION
  9. Section 2: Socio-Economic Development and Implications
    1. Chapter 5: Spidering Scripts for Opinion Monitoring
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    2. Chapter 6: Big Data Dilemmas
      1. ABSTRACT
      2. INTRODUCTION
      3. THE THEORY AND PRACTICE OF BIG DATA
      4. THE ETHICS OF BIG DATA
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    3. Chapter 7: Social Science Data Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SOCIAL SCIENCE DATA MINING
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 8: Data Mining Algorithms for Measuring Performance Impact of Social Development Processes
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN THRUST
      5. FUTURE ISSUES AND CHALLENGES
      6. CONCLUSION
      7. APPENDIX
  10. Section 3: Governance and Applications
    1. Chapter 9: A Customised Dataset to Assist Legal and Ethical Governance of Seaports
      1. ABSTRACT
      2. INTRODUCTION
      3. MAIN FOCUS OF THE CHAPTER
      4. FUTURE RESEARCH DIRECTIONS
      5. CONCLUSION
    2. Chapter 10: Ethical and Legal Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. ORGANIZATIONAL DEVELOPMENT (OD) PRACTITONERS
      4. ORGANIZATIONAL PSYCHOLOGISTS (OP)
      5. DATA MINING
      6. ETHICS
      7. DATA MINING AND LEGALITY
      8. DATA MINING: THE THREE VIEWS
      9. ORGANIZATIONAL DEVELOPMENT PRACTITIONERS AND ORGANIZATIONAL PSYCHOLOGISTS ON DATA MINING
      10. CONCLUSION
    3. Chapter 11: Measuring Student Learning Responsibly
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE POTENTIAL OF LEARNING ANALYTICS
      5. DESIGN CHALLENGE IN LEARNING ANALYTICS
      6. TRENDS OF ANALYTICS CONCERNS FOR SOCIO-ECONOMIC DEVELOPMENT
      7. CONCLUSION
    4. Chapter 12: Improved Decision Support System to Develop a Public Policy to Reduce Dropout Rates for Four Minorities in a Society
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. METHODOLOGY
      5. 4. DATA MINING PROCESS
      6. 5. MINORITY EDUCATION IN MEXICO
      7. 6. STUDY CASE
      8. CONCLUSION
    5. Chapter 13: Digital Rights Management and Corporate Hegemony
      1. ABSTRACT
      2. INTRODUCTION
      3. LEGAL ASPECTS OF DATA MINING
      4. METHOD
      5. DISCUSSION: CORPORATE INFLUENCE ON COPYRIGHT LAW
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
  11. Compilation of References
  12. About the Contributors