You are previewing Data Mining and Analysis in the Engineering Field.
O'Reilly logo
Data Mining and Analysis in the Engineering Field

Book Description

Particularly in the fields of software engineering, virtual reality, and computer science, data mining techniques play a critical role in the success of a variety of projects and endeavors. Understanding the available tools and emerging trends in this field is an important consideration for any organization. Data Mining and Analysis in the Engineering Field explores current research in data mining, including the important trends and patterns and their impact in fields such as software engineering. With a focus on modern techniques as well as past experiences, this vital reference work will be of greatest use to engineers, researchers, and practitioners in scientific-, engineering-, and business-related fields.

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
  8. Acknowledgment
  9. Chapter 1: Optimal Features for Metamorphic Malware Detection
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. MALWARE AND TYPES
    4. 3. MODERN MALWARE
    5. 4. METAMORPHIC MALWARE DETECTION METHODS
    6. 5. DATA MINING TECHNIQUES FOR MALWARE DETECTION
    7. 6. PROPOSED METHODOLOGY
    8. 7. CONCLUSION
    9. 8. OPEN RESEARCH PROBLEMS
    10. ACKNOWLEDGMENT
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  10. Chapter 2: Application of Data Mining and Analysis Techniques for Renewable Energy Network Design and Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. MOTIVATION
    4. DEMONSTRATION
    5. CONCLUSION
    6. REFERENCES
    7. ADDITIONAL READING
    8. KEY TERMS AND DEFINITIONS
  11. Chapter 3: Visualizing the Bug Distribution Information Available in Software Bug Repositories
    1. ABSTRACT
    2. INTRODUCTION
    3. VISUAL ANALYTICS AND VISUAL DATA MINING
    4. SOFTWARE REPOSITORIES
    5. SOFTWARE BUG AS DATA
    6. SOFTWARE BUG STATES
    7. SOFTWARE REPOSITORIES MINING
    8. VISUAL DATA ANALYSIS OF SOFTWARE BUG REPOSITORIES
    9. VISUALIZATION PROCESS
    10. IMPLEMENTATION
    11. SOME VISUALIZATIONS ILLUSTRATIONS
    12. APPLICATIONS OF VISUALIZING THE SOFTWARE BUG REPOSITORIES
    13. CONCLUSION
    14. REFERENCES
  12. Chapter 4: Applications of Data Mining in Software Development Life Cycle
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RESEARCH METHODOLOGY
    4. 3. INTRODUCTION TO SDLC
    5. 4. INTRODUCTION TO DATA MINING
    6. 5. CLASSIFICATION FRAMEWORK
    7. 6. RESEARCH IMPLICATIONS
    8. 7. LIMITATIONS
    9. 8. CONCLUSION
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY WORDS AND DEFINITIONS
  13. Chapter 5: Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques
    1. ABSTRACT
    2. INTRODUCTION
    3. DETAILS OF GPR
    4. DETAILS OF GP
    5. DETAILS OF MPMR
    6. RESULTS AND DISCUSSION
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  14. Chapter 6: Rules Extraction using Data Mining in Historical Data
    1. ABSTRACT
    2. INTRODUCTION
    3. PHASES OF RULES EXTRACTION
    4. PARADIGM OF RULE EXTRACTION
    5. DECISION RULE ALGORITHM
    6. REGRESSION
    7. HYPOTHESIS TESTING ALGORITHM
    8. ROUGH SET RULES
    9. RESEARCH METHODOLOGIES
    10. TOOLS FOR RULE EXTRACTION
    11. ATTRIBUTE SELECTION MEASURE
    12. REMARKS
    13. CONCLUSION
    14. ACKNOWLEDGMENT
    15. REFERENCES
    16. ADDITIONAL READING
    17. KEY TERMS AND DEFINITIONS
  15. Chapter 7: Robust Statistical Methods for Rapid Data Labelling
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND TO THE PROBLEM
    4. DATA INTENSIVE ANALYTICAL TECHNIQUES IN RELIABILITY ENGINEERING
    5. DATASET DESCRIPTION
    6. MULTIVARIATE DISTANCE METRICS
    7. CASE STUDY - NASA BEARING DATA
    8. CASE STUDY – WIND TURBINE GEARBOX
    9. FUTURE RESEARCH DIRECTION
    10. CONCLUSION
    11. REFERENCES
    12. ADDITIONAL READING
    13. APPENDIX 1
    14. APPENDIX 2
    15. APPENDIX 3
    16. APPENDIX 4
    17. APPENDIX 5
    18. APPENDIX 6
  16. Chapter 8: Mathematical Statistical Examinations on Script Relics
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. CLUSTER ANALYSIS AS TOOL OF THE COMPUTATIONAL PALEOGRAPHY
    5. MAIN FOCUS OF THE CHAPTER
    6. ADVANTAGES AND LIMITATIONS
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  17. Chapter 9: Rough Set on Two Universal Sets Based on Multigranulation
    1. ABSTRACT
    2. INTRODUCTION
    3. ROUGH SET ON TWO UNIVERSAL SETS
    4. ROUGH SET ON TWO UNIVERSAL SETS BASED ON MULTIGRANULATION
    5. ALGEBRAIC PROPERTIES OF ROUGH SETS ON TWO UNIVERSAL SETS BASED ON MULTIGRANULATION
    6. GENERALIZATION OF ALGEBRAIC PROPERTIES
    7. KNOWLEDGE REPRESENTATION
    8. MEASURES OF UNCERTAINTY AND ROUGHNESS
    9. PROPERTIES OF MEASURES OF COMPLETENESS AND ROUGHNESS
    10. EXAMPLE FOR COMPUTING COMPLETENESS AND ROUGHNESS
    11. TOPOLOGICAL CHARACTERIZATION
    12. FUTURE RESEARCH DIRECTIONS
    13. CONCLUSION
    14. REFERENCES
    15. ADDITIONAL READING
    16. KEY TERMS AND DEFINITIONS
  18. Chapter 10: Rule Optimization of Web-Logs Data Using Evolutionary Technique
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORKS
    4. 3. OBJECTIVE
    5. 4. PROPOSED SOLUTION
    6. 5. RESULTS AND DISCUSSION
    7. 6. CONCLUSION
    8. 7. RESEARCH METHODOLOGY
    9. REFERENCES
    10. ADDITIONAL READING
    11. KEY TERMS AND DEFINITIONS
  19. Chapter 11: Machine Learning Approaches for Sentiment Analysis
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. MACHINE LEARNING APPROACHES FOR SENTIMENT ANALYSIS
    4. 3. STANDARD DATASETS
    5. 4. EVALUATION MECHANISM
    6. 5. PERFORMANCE COMPARISONS OF VARIOUS METHODS
    7. 6. FUTURE RESEARCH DIRECTIONS
    8. 7. CONCLUSION
    9. REFERENCES
    10. ADDITIONAL READING
    11. KEY TERMS AND DEFINITIONS
  20. Chapter 12: Combining Semantics and Social Knowledge for News Article Summarization
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORKS
    4. THE SOCIONEWSUM SYSTEM
    5. EXPERIMENTS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. ADDITIONAL READING
    10. KEY TERMS AND DEFINITIONS
    11. ENDNOTES
  21. Chapter 13: A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE CHAPTER
    5. CONCLUSION AND FUTURE RESEARCH
    6. REFERENCES
    7. ADDITIONAL READING
    8. KEY TERMS AND DEFINITIONS
  22. Chapter 14: Implementation of Mining Techniques to Enhance Discovery in Service-Oriented Computing
    1. ABSTRACT
    2. INTRODUCTION
    3. ISSUES IN SERVICE DISCOVERY AND RESOLUTIONS
    4. LITERATURE REVIEW
    5. A CASE STUDY
    6. CONCLUSION
    7. REFERENCES
    8. ADDITIONAL READING
    9. KEY TERMS AND DEFINITIONS
    10. APPENDIX
  23. Chapter 15: Overview of Business Intelligence through Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. DATA MINING BASICS
    5. COMPETITIVE ADVANTAGE THROUGH BI
    6. IMPACT OF BUSINESS INTELLIGENCE ON MODERN BUSINESS
    7. CASE STUDY: JAEGER LOCATES LOSSES WITH BI
    8. CASE STUDY: KFC/PIZZA HUT FIND A BETTER BI TOOL
    9. FUTURE CHALLENGES
    10. DISCUSSION AND REMARKS
    11. CONCLUSION
    12. REFERENCES
    13. ADDITIONAL READING
    14. KEY TERMS AND DEFINITIONS
  24. Chapter 16: Population-Based Feature Selection for Biomedical Data Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. FEATURE SELECTION
    4. POPULATION BASED METAHEURISTICS
    5. GENETIC ALGORITHM
    6. PARTICLE SWARM OPTIMIZATION
    7. ARTIFICIAL BEE ALGORITHM
    8. IMPERIALIST COMPETITIVE ALGORITHM
    9. POPULATION BASED FEATURE SELECTION
    10. APPLICATION TO BIOMEDICAL DATA CLASSIFICATION
    11. CONCLUSION
    12. ACKNOWLEDGMENT
    13. REFERENCES
    14. ADDITIONAL READING
    15. KEY TERMS AND DEFINITIONS
  25. Chapter 17: A Comparative Study on Medical Diagnosis Using Predictive Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. PREDECTIVE DATA MINING TECHNIQUES
    5. COMBINATION OF CLASSIFICATION ALGORITHMS
    6. MEDICAL DIAGNOSIS USING PREDICTIVE DATA MINING
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. ADDITIONAL READING
    11. KEY TERMS AND DEFINITIONS
  26. Compilation of References
  27. About the Contributors