You are previewing Web Usage Mining Techniques and Applications Across Industries.
O'Reilly logo
Web Usage Mining Techniques and Applications Across Industries

Book Description

Web usage mining is defined as the application of data mining technologies to online usage patterns as a way to better understand and serve the needs of web-based applications. Because the internet has become a central component in information sharing and commerce, having the ability to analyze user behavior on the web has become a critical component to a variety of industries. Web Usage Mining Techniques and Applications Across Industries addresses the systems and methodologies that enable organizations to predict web user behavior as a way to support website design and personalization of web-based services and commerce. Featuring perspectives from a variety of sectors, this publication is designed for use by IT specialists, business professionals, researchers, and graduate-level students interested in learning more about the latest concepts related to web-based information retrieval and mining.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. List of Reviewers
    1. List of Reviewers
  6. Foreword
  7. Preface
    1. ORGANIZATION OF CHAPTERS
    2. CONCLUSION
    3. REFERENCES
  8. Acknowledgment
  9. Section 1: Concepts and Categorization
    1. Chapter 1: Mastering Web Mining and Information Retrieval in the Digital Age
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. WEB MINING AND INFORMATION RETRIEVAL IN THE DIGITAL AGE
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 2: Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA PRE-PROCESSING WHILE MINING WEB DATA
      4. PROPOSED WORK
      5. APPLICATIONS OF MARKOV CHAIN MODEL
      6. MARKOV MODEL WITH REFRESH STATE
      7. SIMULATION STUDY
      8. CONCLUSION AND FUTURE DIRECTIONS
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Folksonomy-Based Information Retrieval by Generating Tag Cloud for Electronic Resources Management Industries and Suggestive Mechanism for Tagging Using Data Mining Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FOLKSONOMY BASED INFORMATION RETRIEVAL FOR TAG CLOUD AND SUGGESTIVE MECHANISM
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. APPENDIX 1
      10. APPENDIX 2
    4. Chapter 4: Applications of Web Usage Mining across Industries
      1. ABSTRACT
      2. WEB MINING
      3. PROCESS OF WEB USAGE MINING
      4. SOURCES AND TYPES OF DATA
      5. APPLICATIONS OF WEB USAGE MINING
      6. RELATED WORKS
      7. CONCLUSION AND FUTURE RESEARCH
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    5. Chapter 5: Enhancing Web Data Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. MAIN FOCUS OF THE CHAPTER
      4. A GENERAL INTRODUCTION TO WEB DATA-MINING
      5. PRINCIPAL COMPONENT ANALYSIS
      6. GENERALIZED LEAST SQUARE
      7. MAXIMUM LIKELIHOOD REGRESSION
      8. DATASETS INTRODUCTIONS
      9. MARKETING DATASET
      10. BANK DATASET
      11. PARKINSON’S TELE-MONITORING DATASET
      12. METHODOLOGY
      13. INTERPRETATION AND RESULTS
      14. CONCLUSION AND FUTURE WORKS
      15. REFERENCES
      16. ADDITIONAL READING
      17. KEY TERMS AND DEFINITIONS
  10. Section 2: Applications and Analytics
    1. Chapter 6: Application of Conventional Data Mining Techniques and Web Mining to Aid Disaster Management
      1. ABSTRACT
      2. INTRODUCTION
      3. MAIN FOCUS OF THE CHAPTER
      4. WEB MINING TECHNIQUES
      5. LANDSLIDES: TRIGGERING FACTORS, EFFICACY AND SIGNIFICANCE OF DATA MINING TECHNIQUES
      6. DATA MINING TECHNIQUES FOR DISASTER MANAGEMENT: A CONCEPTUAL DEMONSTRATION USING LANDSLIDE AS AN EVENT
      7. FUTURE RESEARCH ISSUES
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
      12. APPENDIX: LIST OF ABBREVIATIONS
    2. Chapter 7: Analyzing Website Quality Issues through Web Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODOLOGY
      5. CONCLUSION AND FUTURE RESEARCH DIRECTION
      6. REFERENCES
      7. ADDITIONAL READING
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 8: Search Query Recommendations in Web Information Retrieval Using Query Logs
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ARCHITECTURE
      5. IDENTIFICATION OF SIMILAR USERS AND QUERIES
      6. CONCLUSION AND FUTURE RESEARCH
      7. REFERENCES
      8. ADDITIONAL READING
      9. APPENDIX: KEY TERMS AND DEFINITIONS
    4. Chapter 9: Web Mining and Analytics for Improving E-Government Services in India
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ROLE OF WEB MINING AND DATA ANALYTICS
      5. CASE STUDY OF MINING AND ANALYTICS IN E-GOVERNANCE
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    5. Chapter 10: A Dynamic and Scalable Decision Tree Based Mining of Educational Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE NEW DYNAMIC AND SCALABLE APPROACH
      5. IMPLEMENTATIONS AND RESULTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
  11. Section 3: Methodologies and Technologies
    1. Chapter 11: Methodologies and Techniques of Web Usage Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. WEB DATA
      5. DATA SOURCES
      6. DATA ABSTRACTIONS
      7. CHALLENGES OF WEB USAGE MINING
      8. WEB USAGE MINING
      9. HOW TO PERFORM WEB USAGE MINING?
      10. METHODOLOGIES OF WEB USAGE MINING
      11. DATA PREPERATION
      12. DATA PREPROCESSING
      13. KNOWLEDGE DISCOVERY
      14. APPLICATIONS
      15. FUTURE RESEARCH DIRECTIONS
      16. CONCLUSION
      17. REFERENCES
      18. KEY TERMS AND DEFINITIONS
    2. Chapter 12: On Visual Information Retrieval Using Multiresolution Techniques for Web Usage Mining Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION AND FUTURE SCOPE
      4. REFERENCES
      5. ADDITIONAL READING
      6. KEY TERMS AND DEFINITIONS
    3. Chapter 13: Effectiveness of Web Usage Mining Techniques in Business Application
      1. ABSTRACT
      2. INTRODUCTION
      3. WEB MINING: AN OVERVIEW
      4. ADDITIONAL READING
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    4. Chapter 14: Web Harvesting
      1. ABSTRACT
      2. INTRODUCTION
      3. WEB DATA EXTRACTION TECHNIQUES
      4. APPLICATIONS
      5. REQUIREMENTS OF A WEB DATA EXTRACTOR
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
  12. Compilation of References
  13. About the Contributors