© 2018

Supervised Descriptive Pattern Mining


  • Covers Exceptional Preference Mining

  • Introduces Subjective Interestingness Measures

  • Presents class association rules and exceptional models within this field


Table of contents

  1. Front Matter
    Pages i-xi
  2. Sebastián Ventura, José María Luna
    Pages 1-31
  3. Sebastián Ventura, José María Luna
    Pages 33-51
  4. Sebastián Ventura, José María Luna
    Pages 53-70
  5. Sebastián Ventura, José María Luna
    Pages 71-98
  6. Sebastián Ventura, José María Luna
    Pages 99-128
  7. Sebastián Ventura, José María Luna
    Pages 129-149
  8. Sebastián Ventura, José María Luna
    Pages 151-170
  9. Sebastián Ventura, José María Luna
    Pages 171-185

About this book


This book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those based on heuristics.  It provides some formal definitions and a general idea about patterns, pattern mining, the usefulness of patterns in the knowledge discovery process, as well as a brief summary on the tasks related to supervised descriptive pattern mining. It also includes a detailed description on the tasks usually grouped under the term supervised descriptive pattern mining: subgroups discovery, contrast sets and emerging patterns. Additionally, this book includes two tasks, class association rules and exceptional models, that are also considered within this field.

A major feature of this book is that it provides a general overview (formal definitions and algorithms) of all the tasks included under the term supervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies for any of these tasks. This book also illustrates how important these techniques are in different fields, a set of real-world applications are described.

Last but not least, some related tasks are also considered and analyzed. The final aim of this book is to provide a general review of the supervised descriptive pattern mining field, describing its tasks, its algorithms, its applications, and related tasks (those that share some common features).

This book  targets developers, engineers and computer scientists aiming to apply classic and heuristic-based algorithms to solve different kinds of pattern mining problems and apply them to real issues. Students and researchers working in this field, can use this comprehensive book (which includes its methods and tools) as a secondary textbook.


Pattern Mining Supervised Descriptive Rule Discovery Subgroup Discovery Contrast Sets Emerging Patterns Exceptional Models Association Rule Mining Frequent Pattern Mining Infrequent Pattern Mining Evolutionary Algorithms Pattern mining quality measures Pattern Mining applications

Authors and affiliations

  1. 1.Computer ScienceUniversity of CordobaCordobaSpain
  2. 2.Computer ScienceUniversity of CordobaCordobaSpain

Bibliographic information