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Pattern Discovery Using Sequence Data Mining

Book Description

Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. List of Reviewers
  5. Preface
  6. Section 1: Current State of Art
    1. Chapter 1: Applications of Pattern Discovery Using Sequential Data Mining
      1. Abstract
      2. HEALTHCARE
      3. EDUCATION
      4. TEXT MINING
      5. BIOINFORMATICS
      6. TELECOMMUNICATIONS
      7. INTRUSION DETECTION
      8. OTHER APPLICATIONS
      9. CONCLUSION
    2. Chapter 2: A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I
      1. Abstract
      2. INTRODUCTION TO SEQUENTIAL PATTERN ANALYSIS USING KERNEL METHODS
      3. KERNEL METHODS FOR PATTERN ANALYSIS
      4. DESIGN OF DYNAMIC KERNELS FOR CONTINUOUS FEATURE VECTOR
      5. REVIEW OF KERNEL METHOD BASED APPROACHES TO CLASSIFICATION AND CLUSTERING OF CONTINUOUS FEATURE VECTOR SEQUENCES
      6. SUMMARY
    3. Chapter 3: A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part II
      1. Abstract
      2. INTRODUCTION
      3. DESIGN OF DYNAMIC KERNELS FOR DISCRETE SYMBOL SEQUENCES
      4. REVIEW OF KERNEL METHODS BASED APPROACHES TO CLASSIFICATION AND CLUSTERING OF DISCRETE SEQUENCES
      5. Conclusion
  7. Section 2: Techniques
    1. Chapter 4: Mining Statistically Significant Substrings Based on the Chi-Square Measure
      1. ABSTRACT
      2. INTRODUCTION
      3. STATISTICAL MODELS AND TOOLS
      4. ALGORITHMS
      5. EXPERIMENTAL RESULTS
      6. CONCLUSION
    2. Chapter 5: Unbalanced Sequential Data Classification using Extreme Outlier Elimination and Sampling Techniques
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. RELATED WORK
      5. MAIN FOCUS OF THE CHAPTER
      6. CONCLUSION
    3. Chapter 6: Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. NEED ASPECT
      5. OVERVIEW OF COMPRESSION SCHEMES
      6. PROPOSED METHODS
      7. EXPERIMENTATION AND RESULTS
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
    4. Chapter 7: Classification of Biological Sequences
      1. Abstract
      2. INTRODUCTION
      3. INTRODUCTION TO BIOLOGICAL SEQUENCES AND DATABASES
      4. ESTIMATING FEATURE PROBABILITIES FROM FAMILY OF BIO-SEQUENCES
      5. RBNBC AND REBMEC CLASSIFIERS
      6. Conclusion
  8. Section 3: Applications
    1. Chapter 8: Approaches for Pattern Discovery Using Sequential Data Mining
      1. Abstract
      2. INTRODUCTION
      3. APPROACHES FOR SEQUENTIAL PATTERN MINING
      4. OTHER SEQUENTIAL PATTERN MINING METHODS
      5. CONCLUSION
    2. Chapter 9: Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques
      1. Abstract
      2. PATTERN DISCOVERY IN KINASES
      3. BACKGROUND
      4. DRUGGABILITY
      5. DISCUSSION AND CONCLUSION
    3. Chapter 10: Identification of Genomic Islands by Pattern Discovery
      1. ABSTRACT
      2. INTRODUCTION
      3. APPROACHES FOR IDENTIFYING GENOMIC ISLANDS
      4. WEB-BASED TOOLS AND DATABASES
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 11: Video Stream Mining for On-Road Traffic Density Analytics
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Traffic Density Estimation APPROAches
      5. VIDEO STREAM MINING FOR TRAFFIC DENSITY ESTIMATION
      6. Conclusion
    5. Chapter 12: Discovering Patterns in Order to Detect Weak Signals and Define New Strategies
      1. Abstract
      2. INTRODUCTION
      3. KNOWLEDGE EXTRACTION PROCESS IN CI
      4. CONSTRUCTION OF THE CORPUS OF DATA
      5. PROCESSING OF DATA STRUCTURE
      6. DATA CROSSBREEDING
      7. EXTRACTION OF STRATEGIC INFORMATION AND THE DISCOVERY OF PATTERNS BY THE CORRESPONDENCE ANALYSIS (CA)
      8. THE PATTERNS OF WEAK SIGNALS
      9. SORT METHOD FOR EXTRACTING WEAK SIGNALS
      10. Interactive Extraction of Information: The Emergence Pattern
      11. TEMPORAL PLACEMENT ALGORITHM BASED ON FORCE DIRECTED PLACEMENT (FDP)
      12. TEMPORAL PLACEMENT ALGORITHM
      13. CONCLUSION
    6. Chapter 13: Discovering Patterns for Architecture Simulation by Using Sequence Mining
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. EPISODE MINING OF EVENT BASED ARCHITECTURE SIMULATION DATA
      5. EXPERIMENTS AND RESULTS
      6. EPISODE MINING TOOL (EMT)
      7. CONCLUSION
    7. Chapter 14: Sequence Pattern Mining for Web Logs
      1. Abstract
      2. INTRODUCTION
      3. SEQUENCE PATTERN MINING USING SUPPORT-INTEREST FRAMEWORK
      4. EXPERIMENTAL RESULTS
      5. CONCLUSION
  9. Compilation of References
  10. About the Contributors