Kiran / Fournier-Viger / Luna Periodic Pattern Mining
1. Auflage 2021
ISBN: 978-981-16-3964-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Theory, Algorithms, and Applications
E-Book, Englisch, 263 Seiten
ISBN: 978-981-16-3964-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.