High-Utility Pattern Mining : Theory, Algorithms and Applications

Philippe Fournier-Viger

ISBN 10: 3030049205 / ISBN 13: 9783030049201

Published by Springer International Publishing, 2019

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data.The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.

Seller Inventory # 9783030049201

Bibliographic Details

• Title: High-Utility Pattern Mining : Theory, Algorithms and Applications
• Publisher: Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3030049205
• ISBN 13: 9783030049201
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 25D millimeters 241 millimeters width by 160 millimeters height by 25 millimeters depth
• Weight: 688 grams

About this title

Synopsis

This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data.

The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.