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Improving Knowledge Discovery through the Integration of Data Mining Techniques

Book Description

Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board
  6. Foreword
  7. Preface
    1. 1. INTRODUCTION
    2. REFERENCES
  8. Section 1:
    1. Chapter 1: Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. PROPOSED METHODOLOGY FOR INTELLIGENT CUBE CONSTRUCTION AND EXPLORATION
      5. EXPERIMENTAL RESULTS AND ANALYSIS ON REAL-WORLD DATASET
      6. CONCLUSION
      7. REFERENCES
    2. Chapter 2: Online Processing of End-User Data in Real-Time Data Warehousing
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. MESHJOIN AND PROBLEM DEFINITION
      5. CACHE-BASED SEMI-STREAM JOIN
      6. PERFORMANCE EXPERIMENTS
      7. CONCLUSIONS
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
    3. Chapter 3: Multi-Relational Data Mining A Comprehensive Survey
      1. ABSTRACT
      2. 1.0 INTRODUCTION
      3. 2.0 MULTI-RELATIONAL DATA MINING
      4. 3.0 MULTI-RELATIONAL DATA MINING LIMITATIONS
      5. 4.0 FUTURE RESEARCH DIRECTIONS IN MRDM
      6. REFERENCES
      7. ADDITIONAL READING
      8. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Comparative Study of Incremental Learning Algorithms in Multidimensional Outlier Detection on Data Stream
      1. ABSTRACT
      2. 1. INTRODUCTION: BACKGROUND OF OUTLIER DETECTION TECHNIQUES
      3. 2. METHODOLOGY OF OUTLIER DETECTION USING MAHALANOBIS DISTANCE (MD)
      4. 3. METHODOLOGY OF OUTLIER DETECTION USING LOCAL OUTLIER FACTOR
      5. 4. EXPERIMENT FOR COMPARISON
      6. 5. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  9. Section 2:
    1. Chapter 5: Advances of Applying Metaheuristics to Data Mining Techniques
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RECENT ADVANCES
      4. 3. CASE STUDY – METAHEURISTICS FOR CLASSIFICATION
      5. 4. CASE STUDY – METAHEURISTICS FOR CLUSTERING
      6. 5. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. ENDNOTES
    2. Chapter 6: Artificial Immune Optimization Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. NATURAL IMMUNE SYSTEM – AN INTRODUCTION
      4. BASIC CONCEPTS OF AN ARTIFICIAL IMMUNE SYSTEM (AIS)
      5. LITERATURE REVIEW
      6. HAIS-OPTIMIZATION ALGORITHM
      7. EXPERIMENTAL RESULTS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 7: The Role of Hypermutation and Affinity Maturation in AIS Approaches to Clustering
      1. ABSTRACT
      2. INTRODUCTION
      3. AIS CLUSTERING: AN OVERVIEW
      4. HUMORAL-MEDIATED CLUSTERING ALGORITHM (HAIS)
      5. PROPOSED THREE-STEP METHODOLOGY
      6. EXPERIMENTAL RESULTS AND DISCUSSION
      7. PROPOSED ENHANCEMENTS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 8: Cancer Pathway Network Analysis Using Cellular Automata
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. MATERIALS AND METHODS
      5. RESULTS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
  10. Section 3:
    1. Chapter 9: Knowledge Extraction from Information System Using Rough Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. INFORMATION SYSTEM
      4. ROUGH SET IN KNOWLEDGE EXTRACTION
      5. ROUGH SET
      6. INDISCERNIBILITY RELATION
      7. ALMOST INDISCERNIBILITY RELATION
      8. ROUGH SET ON FUZZY APPROXIMATION SPACES
      9. ROUGH SET ON INTUITIONISTIC FUZZY APPROXIMATION SPACES
      10. PROPERTIES OF ROUGH SET ON IF APPROXIMATION SPACES
      11. APPLICATION TO DEPENDENCY OF KNOWLEDGE
      12. TOPOLOGICAL CHARACTERIZATION OF ROUGH SET ON IF APPROXIMATION SPACE
      13. ORDERED INFORMATION SYSTEM
      14. AN EXAMPLE OF KNOWLEDGE EXTRACTION
      15. FUTURE RESEARCH DIRECTIONS
      16. CONCLUSION
      17. REFERENCES
      18. KEY TERMS AND DEFINITIONS
    2. Chapter 10: Data Mining Techniques on Earthquake Data
      1. ABSTRACT
      2. INTRODUCTION
      3. PRELIMINARY CONCEPTS AND BASIC METHODS
      4. RELATED WORKS
      5. PROPOSED APPROACH AND RECOMMENDATIONS FOR CASE STUDY (NORTHWEST OF IRAN)
      6. RESULTS AND DISCUSSIONS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 11: An Evaluation of C4.5 and Fuzzy C4.5 with Effect of Pruning Methods
      1. ABSTRACT
      2. INTRODUCTION
      3. MATERIAL AND METHODS
      4. CRITERIA FOR COMPARISON
      5. DECISION TREE
      6. COMPARISON C4.5 AND FUZZY C4.5
      7. Pruning Methods
      8. COMPARISON OF PRUNING METHODS
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    4. Chapter 12: An Empirical Evaluation of Feature Selection Methods
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND AND RELATED WORK
      4. 3. FEATURE SELECTION METHODS
      5. 4. DATASET
      6. 5. RESULTS AND DISCUSSION
      7. 6. CONCLUSION AND FUTURE WORK
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  11. Section 4:
    1. Chapter 13: Data Mining Driven Rule Based Expert System for Medical Billing Compliance
      1. ABSTRACT
      2. INTRODUCTION
      3. PROBLEM STATEMENT
      4. PROPOSED ARCHITECTURE OF DATA MINING DRIVEN RULE BASED EXPERT SYSTEM
      5. IMPLEMENTATION OF DATA MINING DRIVEN RULE BASED SYSTEM FOR MEDICAL BILLING
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 14: A Web Backtracking Technique for Fraud Detection in Financial Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CONCLUSION
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONS
    3. Chapter 15: Segmentation of Crops and Weeds Using Supervised Learning Technique
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. ARTIFICIAL NEURAL NETWORK
      4. 3. EDGE DETECTION USING FUZZY LOGIC
      5. 4. PROPOSED METHODOLOGY
      6. 5. SIMULATION AND RESULTS
      7. 6. CONCLUSION AND FUTURE WORK
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    4. Chapter 16: A Supervised Learning Model for AGV Perception in Unstructured Environment
      1. ABSTRACT
      2. INTRODUCTION
      3. EXPERIMENTATIONS AND RECOMMENDATIONS
      4. DATA ACQUISITION
      5. IMAGE PROCESSING
      6. ROAD EXTRACTION
      7. EXPERIMENTS, RESULTS, AND DISCUSSIONS
      8. DATA ACQUISITION
      9. PREPROCESSING
      10. ROAD EXTRACTION (K-MEAN CLUSTERING)
      11. ROAD EXTRACTION (SUPPORT VECTOR MACHINE CLASSIFICATION)
      12. RESULTS FOR DIFFERENT DATA SET
      13. CONCLUSION AND FUTURE WORK
      14. REFERENCES
      15. KEY TERMS AND DEFINITIONS
  12. Compilation of References
  13. About the Contributors