Periodic Pattern Mining

1. Front Matter

   Pages i-viii

   PDF

2. Introduction to Data Mining

   • Jose M. Luna

   Pages 1-22

3. Discovering Frequent Patterns in Very Large Transactional Databases

   • Jose M. Luna

   Pages 23-40

4. Discovering Full Periodic Patterns in Temporal Databases

   • Pamalla Veena, R. Uday Kiran

   Pages 41-56

5. Discovering Fuzzy Periodic Patterns in Quantitative Temporal Databases

   • Pamalla Veena, R. Uday Kiran, Penugonda Ravikumar, Sonali Aggrawal

   Pages 57-67

6. Discovering Partial Periodic Patterns in Temporal Databases

   • Pamalla Veena, Palla Likhitha, B. Sai chithra, R. Uday Kiran

   Pages 69-79

7. Finding Periodic Patterns in Multiple Sequences

   • Philippe Fournier-Viger, Tin Truong Chi, Youxi Wu, Jun-Feng Qu, Jerry Chun-Wei Lin, Zhitian Li

   Pages 81-103

8. Discovering Self-reliant Periodic Frequent Patterns

   • Vincent Mwintieru Nofong, Hamidu Abdel-Fatao, Michael Kofi Afriyie, John Wondoh

   Pages 105-131

9. Discovering Periodic High Utility Itemsets in a Discrete Sequence

   • Philippe Fournier-Viger, Youxi Wu, Duy-Tai Dinh, Wei Song, Jerry Chun-Wei Lin

   Pages 133-151

10. Mining Periodic High-Utility Sequential Patterns with Negative Unit Profits

    • Ut Huynh, Bac Le, Duy-Tai Dinh, Van-Nam Huynh

    Pages 153-170

11. Hiding Periodic High-Utility Sequential Patterns

    • Ut Huynh, Bac Le, Duy-Tai Dinh

    Pages 171-189

12. NetHAPP: High Average Utility Periodic Gapped Sequential Pattern Mining

    • Youxi Wu, Meng Geng, Yan Li, Lei Guo, Philippe Fournier-Viger

    Pages 191-214

13. Privacy Preservation of Periodic Frequent Patterns Using Sensitive Inverse Frequency

    • Usman Ahmed, Jerry Chun-Wei Lin, Philippe Fournier-Viger

    Pages 215-227

14. Real-World Applications of Periodic Patterns

    • R. Uday Kiran, Masashi Toyoda, Koji Zettsu

    Pages 229-235

15. Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data

    • Minh-Son Dao, R. Uday Kiran, Koji Zettsu

    Pages 237-263

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.

• Data Mining
• Big Data Analytics
• Pattern Mining
• Frequent Patterns
• Periodic Patterns
• Time series
• Transactional databases
• Algorithms
• Applications

• Division of Information Systems, University of Aizu, Aizu-Wakamatsu, Japan

  R. Uday Kiran

• College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

  Philippe Fournier-Viger

• Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

  Jose M. Luna

• Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

  Jerry Chun-Wei Lin

• Department of Computer Science, Ashoka University, Sonepat, India

  Anirban Mondal

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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