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Data Mining Applications for Empowering Knowledge Societies

Book Description

Data Mining Applications for Empowering Knowledge Societies presents an overview on the main issues of data mining, including its classification, regression, clustering, and ethical issues. This comprehensive book also provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.

Table of Contents

  1. Copyright
  2. Foreword
  3. Preface
  4. REFERENCES
  5. Acknowledgment
  6. Section I: Education and Research
  7. Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications
    1. INTRODUCTION
    2. MULTIPLE CRITERIA OPTIMIZATION-BASED CLASSIFICATION MODELS
    3. REAL-LIFE APPLICATIONS USING MULTIPLE CRITERIA OPTIMIZATION APPROACHES
    4. RESEARCH CHALLENGES AND OPPORTUNITIES
    5. CONCLUSION
    6. ACKNOWLEDGMENT
  8. REFERENCES
  9. Making Decisions with Data: Using Computational Intelligence Within a Business Environment
    1. INTRODUCTION
    2. BACKGROUND
    3. BEING COMMERCIAL
    4. CONCEPTUAL, CULTURAL, AND TECHNICAL BARRIERS
    5. FUTURE TRENDS
    6. SUMMARY
  10. REFERENCES
  11. Data Mining Association Rules for Making Knowledgeable Decisions
    1. INTRODUCTION
    2. BACKGROUND
    3. TECHNIQUES USED FOR FINDING FREQUENT ITEMSETS
    4. FUTURE TRENDS
    5. CONCLUSION
  12. REFERENCES
  13. Section II: Tools, Techniques, Methods
  14. Image Mining: Detecting Deforestation Patterns Through Satellites
    1. INTRODUCTION
    2. REMOTE SENSING AND IMAGE MINING
    3. CHALLENGES AND TECHNOLOGICAL STRATEGIES ON DEFORESTATION ISSUE
    4. DETECTING DEFORESTATION PATTERNS THROUGH SATELLITES
    5. FUTURE TRENDS
    6. CONCLUSION
  15. REFERENCES
  16. Machine Learning and Web Mining: Methods and Applications in Societal Benefit Areas
    1. INTRODUCTION
    2. WEB MINING OVERVIEW
    3. MACHINE LEARNING OVERVIEW
    4. MACHINE LEARNING APPLIED TO WEB MINING
    5. APPLICATIONS OF WEB MINING TO SOCIETAL BENEFIT AREAS
    6. FUTURE TRENDS
    7. CONCLUSION
  17. REFERENCES
  18. The Importance of Data Within Contemporary CRM
    1. INTRODUCTION
    2. PROCESSES: THE KEY TO UNLOCKING THE SECRETS OF DATA
    3. THE DATABASE: THE PIVOTAL TOOL OF CRM
    4. CONVERTING DATA INTO COMPETITIVE EDGE
    5. RETAINING OLD CUSTOMERS AND REACHING NEW ONES
    6. FUTURE TRENDS
    7. CONCLUSION
  19. REFERENCES
  20. Mining Allocating Patterns in Investment Portfolios
    1. INTRODUCTION
    2. RELATED WORK
    3. ALLOCATING PATTERNS
    4. ALLOCATION PATTERN MINING
    5. APPLYING ALPS IN PORTFOLIO MANAGEMENT
    6. EXPERIMENTAL RESULTS
    7. CONCLUSION AND FUTURE RESEARCH
    8. ACKNOWLEDGMENT
  21. REFERENCES
    1. ENDNOTE
  22. Application of Data Mining Algorithms for Measuring Performance Impact of Social Development Activities
    1. INTRODUCTION
    2. BACKGROUND
    3. MAIN THRUST
    4. FUTURE ISSUES AND CHALLENGES
    5. CONCLUSION
  23. REFERENCES
    1. ENDNOTES
  24. Section III: Applications of Data Mining
  25. Prospects and Scopes of Data Mining Applications in Society Development Activities
    1. INTRODUCTION
    2. BACKGROUND
    3. MAIN THRUST
    4. FUTURE ISSUES AND CHALLENGES
    5. CONCLUSION
  26. REFERENCES
    1. ENDNOTES
  27. Business Data Warehouse: The Case of Wal-Mart
    1. INTRODUCTION
    2. BACKGROUND
    3. MAIN THRUST
    4. FUTURE TRENDS
    5. CONCLUSION
  28. REFERENCES
  29. Medical Applications of Nanotechnology in the Research Literature1
    1. INTRODUCTION
    2. SUMMARY OF OVERALL NANOTECHNOLOGY STUDY
    3. BACKGROUND
    4. APPROACH
    5. RESULTS
    6. SUMMARY AND CONCLUSION
  30. REFERENCES
    1. ENDNOTE
  31. Early Warning System for SMEs as a Financial Risk Detector
    1. INTRODUCTION
    2. BACKGROUND
    3. MAIN THRUST
    4. EARLY WARNING SYSTEM FOR SMES BASED ON DATA MINING
    5. THE FUTURE VISION OF EARLY WARNING SYSTEMS BASED ON DATA MINING
    6. CONCLUSION
  32. REFERENCES
  33. What Role is "Business Intelligence" Playing in Developing Countries? A Picture of Brazilian Companies
    1. INTRODUCTION
    2. BACKGROUND
    3. MAIN THRUST
    4. RESULTS
    5. RECOMMENDATIONS AND FUTURE DIRECTIONS
    6. CONCLUSION
  34. REFERENCES
  35. Building an Environmental GIS Knowledge Infrastructure
    1. INTRODUCTION
    2. BACKGROUND
    3. KNOWLEDGE DISCOVERY INTERFACE
    4. POTENTIAL APPLICATIONS OF THE KDI
    5. KDI IMPLEMENTATION
    6. FUTURE ISSUES
    7. CONCLUSION
  36. REFERENCES
  37. The Application of Data Mining for Drought Monitoring and Prediction
    1. INTRODUCTION
    2. BACKGROUND
    3. CURRENT RESEARCH USING DATA MINING
    4. FUTURE TRENDS
    5. CONCLUSION
  38. REFERENCES
  39. Compilation of References
  40. About the Contributors
  41. Index