Periodic Pattern Mining
Beschreibung:
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.
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.