Supervised Descriptive Pattern Mining

Sebastián Ventura (u. a.)

ISBN 10: 3030074560 / ISBN 13: 9783030074562

Published by Springer, 2019

About this Item

Supervised Descriptive Pattern Mining | Sebastián Ventura (u. a.) | Taschenbuch | xi | Englisch | 2019 | Springer | EAN 9783030074562 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 117500414

Bibliographic Details

• Title: Supervised Descriptive Pattern Mining
• Publisher: Springer
• Publication Date: 2019
• Language: English
• ISBN 10: 3030074560
• ISBN 13: 9783030074562
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 312 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.