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Biologically-Inspired Techniques for Knowledge Discovery and Data Mining

Book Description

Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.

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. Chapter 1: Biologically Inspired Techniques for Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. SWARM INTELLIGENCE
    4. PARTICLE SWARM OPTIMIZATION
    5. PARTICLE SWARM OPTIMIZATION FOR DATA CLUSTERING
    6. PSO BASED OUTLIER DETECTION
    7. PSO BASED RECOMMENDER SYSTEMS
    8. FUTURE WORK
    9. CONCLUSION
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  9. Chapter 2: Probabilistic Control and Swarm Dynamics in Mobile Robots and Ants
    1. ABSTRACT
    2. INTRODUCTION
    3. COMMON DYNAMIC MEMORY AND COMMUNICATION IN A SWARM
    4. CONTROL OF MOBILE AGENTS AND THE LOCOMOTION MODEL
    5. MOTION OF A SINGLE MOBILE AGENT AND THE USE OF COMMON MEMORY
    6. SWARM DYNAMICS WITH COMMUNICATION AND COMMON MEMORY
    7. DISCUSSION AND FURTHER RESEARCH DIRECTIONS
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  10. Chapter 3: A Measure Optimized Cost-Sensitive Learning Framework for Imbalanced Data Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MEASURE OPTIMIED COST SENSITIVE LEARNING
    5. EXPERIMENTAL STUDY
    6. CONCLUSION
    7. FUTURE RESEARCH
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  11. Chapter 4: Towards an Improved Ensemble Learning Model of Artificial Neural Networks
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. RESEARCH METHODOLOGY
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. ADDITIONAL READING
    10. KEY TERMS AND DEFINITIONS
  12. Chapter 5: Ant Programming Algorithms for Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. THE GBAP ALGORITHM: GRAMMAR-BASED ANT PROGRAMMING
    4. THE MOGBAP ALGORITHM: MULTI-OBJECTIVE GRAMMAR-BASED ANT PROGRAMMING
    5. THE APIC ALGORITHM: ANT PROGRAMMING FOR IMBALANCED CLASSIFICATION
    6. EXPERIMENTAL STUDIES
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
    10. ENDNOTES
  13. Chapter 6: Machine Fault Diagnosis and Prognosis using Self-Organizing Map
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. SELF-ORGANIZING MAP (SOM)
    5. 4. EXPERIMENTAL SETUP
    6. 5. FEATURE EXTRACTION
    7. 6. EXPERIMENT AND ITS RESULTS
    8. 7. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  14. Chapter 7: An Enhanced Artificial Bee Colony Optimizer for Predictive Analysis of Heating Oil Prices using Least Squares Support Vector Machines
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE REVIEW
    4. ESTIMATION MODEL BASED ON LSSVM
    5. OPTIMIZATION BASED ON ABC ALGORITHM
    6. THE PROPOSED ABC-LSSVM
    7. DATA DESCRIPTION AND TRAINING PROCEDURE
    8. RESULTS AND DISCUSSION
    9. FUTURE RESEARCH DIRECTIONS
    10. CONCLUSION
    11. REFERENCES
    12. ADDITIONAL READING
    13. KEY TERMS AND DEFINITIONS
  15. Chapter 8: Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATIONAL KNOWLEDGE AND DEPENDENCIES IN DATA
    4. ROLE OF FUZZY LOGIC IN DATA MINING
    5. FUZZY FUNCTIONAL DEPENDENCIES
    6. LS FOR EXTRACTING RELATIONAL KNOWLEDGE IN DATA
    7. CONSTRUCTION OF MEMBERSHIP FUNCTIONS
    8. CASE STUDIES
    9. DIRECTIONS FOR FUTURE RESEARCH
    10. CONCLUSION
    11. REFERENCES
    12. ADDITIONAL READING
    13. KEY TERMS AND DEFINITIONS
  16. Chapter 9: Application of Artificial Neural Network and Genetic Programming in Civil Engineering
    1. ABSTRACT
    2. INTRODUCTION
    3. EXPERIMENTAL PROCEDURE
    4. RESULTS AND DISCUSSION
    5. CONCLUSION
    6. ACKNOWLEDGMENT
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  17. Chapter 10: A Promising Direction towards Automatic Construction of Relevance Measures
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM STATEMENT
    4. WRAPPING LEARNING ALGORITHMS TO MEASURE RELEVANCE
    5. THE LACK OF GENERALITY IN WRAPPER AND HEURISTIC MEASURES
    6. EVOLVING RELEVANCE MEASURES
    7. TWO EXAMPLE CASE STUDIES
    8. DISCUSSION
    9. CONCLUSION
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  18. Chapter 11: Adaptive Scheduling for Real-Time Distributed Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. THE SYSTEM AND TASK MODEL
    4. THE PROPOSED ALGORITHM
    5. SIMULATION METHOD
    6. EXPERIMENTAL RESULTS
    7. CONCLUSION
    8. FUTURE RESEARCH DIRECTIONS
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  19. Chapter 12: Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms
    1. ABSTRACT
    2. INTRODUCTION
    3. A BRIEF BACKGROUND ON EMERGE-SORT
    4. DISCOVERING VARIANTS OF EMERGE-SORT
    5. DISCUSSION AND RESEARCH DIRECTIONS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
    10. ENDNOTES
    11. APPENDIX: 3-LONG NEIGHBORHOODS AND ALTERNATING RUNS
  20. Chapter 13: Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MOTIVATION
    5. DATA SOURCE
    6. PROBLEM STATEMENT
    7. GENERAL METHODOLOGY
    8. CASE STUDY
    9. CONCLUSION
    10. FUTURE RESEARCH DIRECTIONS
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  21. Chapter 14: Online Prediction of Blood Glucose Levels using Genetic Algorithm
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. METHODOLOGY
    5. RESULTS AND DISCUSSION
    6. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  22. Chapter 15: Security of Wireless Devices using Biological-Inspired RF Fingerprinting Technique
    1. ABSTRACT
    2. INTRODUCTION
    3. PHY LAYER SECURITY
    4. UNIVERSAL SOFTWARE RADIO PERIPHERAL
    5. CLASSIFICATION METHODOLOGY
    6. ARTIFICIAL NEURAL NETWORK
    7. PERFORMANCE EVALUATION
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  23. Compilation of References
  24. About the Contributors