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.
