Periodic Pattern Mining: Theory, Algorithms, and Applications

This book provides an introduction to periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. It is a popular and emerging research area in data mining that involves discovering all regularly occurring patterns in temporal databases. The book consists of three main parts: introduction, algorithms, and applications. It covers techniques for discovering periodic patterns in transactional databases, temporal databases, quantitative temporal databases, and big data. It also presents concise representations of periodic patterns and privacy-preserving data mining techniques. Several applications of periodic pattern mining are discussed, including air pollution data analytics, accident data analytics, and traffic congestion analytics.

Format: Paperback / softback
Length: 263 pages
Publication date: 30 October 2022
Publisher: Springer Verlag, Singapore

Periodic pattern mining is a rapidly evolving field within data mining that focuses on identifying all regularly occurring patterns in temporal databases. This technique has numerous applications, particularly in analyzing customer transaction databases to uncover sets of items that are frequently purchased by customers. By understanding these patterns, businesses can gain valuable insights into customer behavior and tailor their marketing strategies accordingly.

The book "Periodic Pattern Mining: An Introduction" serves as a comprehensive guide to this field. It provides an introduction to pattern mining and periodic pattern mining, covering key concepts such as periodicity, periodic support, search space exploration techniques, and pruning strategies. The book then presents various algorithms for discovering periodic patterns, including periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithms.

The subsequent chapters delve into state-of-the-art techniques for discovering periodic patterns in different types of databases, including transactional databases, temporal databases, quantitative temporal databases, and big data. The theory on concise representations of periodic patterns is also discussed, along with privacy-preserving data mining techniques for protecting sensitive information.

The book concludes with several practical applications of periodic pattern mining, including air pollution data analytics and accident data analytics. These applications demonstrate how periodic pattern mining can be used to solve real-world problems and make informed decisions.

Overall, "Periodic Pattern Mining: An Introduction" is an essential resource for anyone interested in data mining and discovering patterns in temporal databases. It provides a comprehensive overview of the field, covering both theoretical concepts and practical applications, and is suitable for both researchers and practitioners.

Weight: 421g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811639661
Edition number: 1st ed. 2021