Contrast Data Mining

Contrast Data Mining

by Guozhu Dong, James Bailey
Released April 2016
Publisher(s): Chapman and Hall/CRC
ISBN: 9781439854334

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Book description

This work collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. It examines how contrast mining is used in discriminative gene transfer and microarray analysis, computational toxicology, spatial and image data classification, network security, and many more applications.

Table of contents

  1. Front Cover
  2. Contrast Data Mining: Concepts, Algorithms, and Applications
  3. Copyright
  4. Dedication
  5. Table of Contents (1/2)
  6. Table of Contents (2/2)
  7. Foreword
  8. Preface
  9. Part I: Preliminaries and Statistical Contrast Measures
    1. 1. Preliminaries (1/2)
    2. 1. Preliminaries (2/2)
    3. 2. Statistical Measures for Contrast Patterns (1/2)
    4. 2. Statistical Measures for Contrast Patterns (2/2)
  10. Part II: Contrast Mining Algorithms
    1. 3. Mining Emerging Patterns Using Tree Structures or Tree Based Searches (1/2)
    2. 3. Mining Emerging Patterns Using Tree Structures or Tree Based Searches (2/2)
    3. 4. Mining Emerging Patterns Using Zero-Suppressed Binary Decision Diagrams (1/2)
    4. 4. Mining Emerging Patterns Using Zero-Suppressed Binary Decision Diagrams (2/2)
    5. 5. Efficient Direct Mining of Selective Discriminative Patterns for Classification (1/4)
    6. 5. Efficient Direct Mining of Selective Discriminative Patterns for Classification (2/4)
    7. 5. Efficient Direct Mining of Selective Discriminative Patterns for Classification (3/4)
    8. 5. Efficient Direct Mining of Selective Discriminative Patterns for Classification (4/4)
    9. 6. Mining Emerging Patterns from Structured Data (1/2)
    10. 6. Mining Emerging Patterns from Structured Data (2/2)
    11. 7. Incremental Maintenance of Emerging Patterns (1/4)
    12. 7. Incremental Maintenance of Emerging Patterns (2/4)
    13. 7. Incremental Maintenance of Emerging Patterns (3/4)
    14. 7. Incremental Maintenance of Emerging Patterns (4/4)
  11. Part III: Generalized Contrasts, Emerging Data Cubes, and Rough Sets
    1. 8. More Expressive Contrast Patterns and Their Mining (1/4)
    2. 8. More Expressive Contrast Patterns and Their Mining (2/4)
    3. 8. More Expressive Contrast Patterns and Their Mining (3/4)
    4. 8. More Expressive Contrast Patterns and Their Mining (4/4)
    5. 9. Emerging Data Cube Representations for OLAP Database Mining (1/4)
    6. 9. Emerging Data Cube Representations for OLAP Database Mining (2/4)
    7. 9. Emerging Data Cube Representations for OLAP Database Mining (3/4)
    8. 9. Emerging Data Cube Representations for OLAP Database Mining (4/4)
    9. 10. Relation Between Jumping Emerging Patterns and Rough Set Theory (1/4)
    10. 10. Relation Between Jumping Emerging Patterns and Rough Set Theory (2/4)
    11. 10. Relation Between Jumping Emerging Patterns and Rough Set Theory (3/4)
    12. 10. Relation Between Jumping Emerging Patterns and Rough Set Theory (4/4)
  12. Part IV: Contrast Mining for Classification & Clustering
    1. 11. Overview and Analysis of Contrast Pattern Based Classification (1/4)
    2. 11. Overview and Analysis of Contrast Pattern Based Classification (2/4)
    3. 11. Overview and Analysis of Contrast Pattern Based Classification (3/4)
    4. 11. Overview and Analysis of Contrast Pattern Based Classification (4/4)
    5. 12. Using Emerging Patterns in Outlier and Rare-Class Prediction (1/4)
    6. 12. Using Emerging Patterns in Outlier and Rare-Class Prediction (2/4)
    7. 12. Using Emerging Patterns in Outlier and Rare-Class Prediction (3/4)
    8. 12. Using Emerging Patterns in Outlier and Rare-Class Prediction (4/4)
    9. 13. Enhancing Traditional Classifiers Using Emerging Patterns (1/2)
    10. 13. Enhancing Traditional Classifiers Using Emerging Patterns (2/2)
    11. 14. CPC: A Contrast Pattern Based Clustering Algorithm (1/4)
    12. 14. CPC: A Contrast Pattern Based Clustering Algorithm (2/4)
    13. 14. CPC: A Contrast Pattern Based Clustering Algorithm (3/4)
    14. 14. CPC: A Contrast Pattern Based Clustering Algorithm (4/4)
  13. Part V: Contrast Mining for Bioinformatics and Chemoinformatics
    1. 15. Emerging Pattern Based Rules Characterizing Subtypes of Leukemia (1/3)
    2. 15. Emerging Pattern Based Rules Characterizing Subtypes of Leukemia (2/3)
    3. 15. Emerging Pattern Based Rules Characterizing Subtypes of Leukemia (3/3)
    4. 16. Discriminating Gene Transfer and Microarray Concordance Analysis (1/2)
    5. 16. Discriminating Gene Transfer and Microarray Concordance Analysis (2/2)
    6. 17. Towards Mining Optimal Emerging Patterns Amidst 1000s of Genes (1/3)
    7. 17. Towards Mining Optimal Emerging Patterns Amidst 1000s of Genes (2/3)
    8. 17. Towards Mining Optimal Emerging Patterns Amidst 1000s of Genes (3/3)
    9. 18. Emerging Chemical Patterns – Theory and Applications (1/4)
    10. 18. Emerging Chemical Patterns – Theory and Applications (2/4)
    11. 18. Emerging Chemical Patterns – Theory and Applications (3/4)
    12. 18. Emerging Chemical Patterns – Theory and Applications (4/4)
    13. 19. Emerging Patterns as Structural Alerts for Computational Toxicology (1/3)
    14. 19. Emerging Patterns as Structural Alerts for Computational Toxicology (2/3)
    15. 19. Emerging Patterns as Structural Alerts for Computational Toxicology (3/3)
  14. Part VI: Contrast Mining for Special Domains
    1. 20. Emerging Patterns and Classification for Spatial and Image Data (1/4)
    2. 20. Emerging Patterns and Classification for Spatial and Image Data (2/4)
    3. 20. Emerging Patterns and Classification for Spatial and Image Data (3/4)
    4. 20. Emerging Patterns and Classification for Spatial and Image Data (4/4)
    5. 21. Geospatial Contrast Mining with Applications on Labeled Spatial Data (1/3)
    6. 21. Geospatial Contrast Mining with Applications on Labeled Spatial Data (2/3)
    7. 21. Geospatial Contrast Mining with Applications on Labeled Spatial Data (3/3)
    8. 22. Mining Emerging Patterns for Activity Recognition (1/3)
    9. 22. Mining Emerging Patterns for Activity Recognition (2/3)
    10. 22. Mining Emerging Patterns for Activity Recognition (3/3)
    11. 23. Emerging Pattern Based Prediction of Heart Diseases and Powerline Safety (1/2)
    12. 23. Emerging Pattern Based Prediction of Heart Diseases and Powerline Safety (2/2)
    13. 24. Emerging Pattern Based Crime Spots Analysis and Rental Price Prediction (1/3)
    14. 24. Emerging Pattern Based Crime Spots Analysis and Rental Price Prediction (2/3)
    15. 24. Emerging Pattern Based Crime Spots Analysis and Rental Price Prediction (3/3)
  15. Part VII: Survey of Other Papers
    1. 25. Overview of Results on Contrast Mining and Applications (1/2)
    2. 25. Overview of Results on Contrast Mining and Applications (2/2)
  16. Bibliography (1/8)
  17. Bibliography (2/8)
  18. Bibliography (3/8)
  19. Bibliography (4/8)
  20. Bibliography (5/8)
  21. Bibliography (6/8)
  22. Bibliography (7/8)
  23. Bibliography (8/8)
  24. Back Cover

Product information

  • Title: Contrast Data Mining
  • Author(s): Guozhu Dong, James Bailey
  • Release date: April 2016
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781439854334