Periodic pattern mining : theory, algorithms, and applications / R. Uday Kiran, Philippe Fournier-Viger, Jose M. Luna, Jerry Chun-Wei Lin, Anirban Mondal, editors.
Kiran, R. Uday, editor.; Fournier-Viger, Philippe, editor.; Luna, Jose M., editor.; Lin, Jerry Chun-Wei, editor.; Mondal, Anirban, editor.
2021
QA76.9.D343 P47 2021
Available Online

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Title Periodic pattern mining : theory, algorithms, and applications / R. Uday Kiran, Philippe Fournier-Viger, Jose M. Luna, Jerry Chun-Wei Lin, Anirban Mondal, editors.
ISBN 9789811639647 (electronic bk.)
9811639647 (electronic bk.)
9789811639630
9811639639
DOI https://doi.org/10.1007/978-981-16-3964-7
Published Singapore : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource : illustrations (some color)
Other Standard Identifiers 10.1007/978-981-16-3964-7 doi
Call Number QA76.9.D343 P47 2021
Dewey Decimal Classification 006.3/12
Summary This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 16, 2021).
Added Author Kiran, R. Uday, editor.
Fournier-Viger, Philippe, editor.
Luna, Jose M., editor.
Lin, Jerry Chun-Wei, editor.
Mondal, Anirban, editor.
Available in Other Form Periodic pattern mining.
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Chapter 1: Introduction to Data Mining
Chapter 2: Discovering Frequent Patterns in Very Large Transactional Database
Chapter 3: Discovering Periodic Frequent Patterns in Temporal Databases
Chapter 4: Discovering Fuzzy Periodic Frequent Patterns in Quantitative Temporal Databases
Chapter 5: Discovering Partial Periodic Patterns in Temporal Databases
Chapter 6: Finding Periodic Patterns in Multiple Sequences
Chapter 7: Discovering Self Reliant Patterns
Chapter 8: Finding Periodic High Utility Patterns in Sequence
Chapter 9: Mining Periodic High Utility Sequential Patterns with Negative Unit Profits
Chapter 10: Hiding Periodic High Utility Sequential Patterns
Chapter 11: NetHAPP
Chapter 12: Privacy Preservation of Periodic Frequent Patterns using Sensitive Inverse Frequency.

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