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.
表中的内容
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: Net HAPP.- Chapter 12: Privacy Preservation of Periodic Frequent Patterns using Sensitive Inverse Frequency.
关于作者
Rage Uday Kiran is an associate professor at the University of Aizu in Japan. He has published over 50 articles in refereed journals and international conferences, such as EDBT, SSDBM, CIKM, IEEE FUZZY, PAKDD, DASFAA, DEXA and JSS. His current research interests include data mining, parallel computation, air pollution data analytics, traffic congestion data analytics, recommender systems, and ICTs for Agriculture.
Philippe Fournier-Viger is professor at the Harbin Institute of Technology. He has published more than 300 research papers with over 7200 citations. He is Associate Editor-in-Chief of Applied Intelligence and founder of the SPMF pattern mining library.
Jose Maria Luna is an assistant professor at the University of Cordoba, Spain. He received the Ph.D. degree in Computer Science from the University of Granada, Spain. He has published more than 30 papers in top ranked journals, most of them in the pattern mining field. He is author of two books, related to pattern mining, published by Springer: ‘Pattern Mining with Evolutionary Algorithms’ and ‘Supervised Descriptive Pattern mining”
Jerry Chun-Wei Lin received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published more than 400 research articles in several top-tier conferences and journals. He has recognized as the most cited Chinese Researcher respectively in 2018, 2019, and 2020 by Scopus/Elsevier. He is the Fellow of IET (FIET), senior member for both IEEE and ACM.
Anirban Mondal an Associate Professor of Computer Science at Ashoka University, India. His research interests include database indexing, spatial databases, mobile data management, big data analytics and utility mining. He specializes in the domains of finance, retail and smart cities.
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