Frequent Pattern Mining

1. Front Matter

   Pages i-xix

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2. An Introduction to Frequent Pattern Mining

   • Charu C. Aggarwal

   Pages 1-17

3. Frequent Pattern Mining Algorithms: A Survey

   • Charu C. Aggarwal, Mansurul A. Bhuiyan, Mohammad Al Hasan

   Pages 19-64

4. Pattern-Growth Methods

   • Jiawei Han, Jian Pei

   Pages 65-81

5. Mining Long Patterns

   • Feida Zhu

   Pages 83-104

6. Interesting Patterns

   • Jilles Vreeken, Nikolaj Tatti

   Pages 105-134

7. Negative Association Rules

   • Luiza Antonie, Jundong Li, Osmar Zaiane

   Pages 135-145

8. Constraint-Based Pattern Mining

   • Siegfried Nijssen, Albrecht Zimmermann

   Pages 147-163

9. Mining and Using Sets of Patterns through Compression

   • Matthijs van Leeuwen, Jilles Vreeken

   Pages 165-198

10. Frequent Pattern Mining in Data Streams

    • Victor E. Lee, Ruoming Jin, Gagan Agrawal

    Pages 199-224

11. Big Data Frequent Pattern Mining

    • David C. Anastasiu, Jeremy Iverson, Shaden Smith, George Karypis

    Pages 225-259

12. Sequential Pattern Mining

    • Wei Shen, Jianyong Wang, Jiawei Han

    Pages 261-282

13. Spatiotemporal Pattern Mining: Algorithms and Applications

    • Zhenhui Li

    Pages 283-306

14. Mining Graph Patterns

    • Hong Cheng, Xifeng Yan, Jiawei Han

    Pages 307-338

15. Uncertain Frequent Pattern Mining

    • Carson Kai-Sang Leung

    Pages 339-367

16. Privacy Issues in Association Rule Mining

    • Aris Gkoulalas-Divanis, Jayant Haritsa, Murat Kantarcioglu

    Pages 369-401

17. Frequent Pattern Mining Algorithms for Data Clustering

    • Arthur Zimek, Ira Assent, Jilles Vreeken

    Pages 403-423

18. Supervised Pattern Mining and Applications to Classification

    • Albrecht Zimmermann, Siegfried Nijssen

    Pages 425-442

19. Applications of Frequent Pattern Mining

    • Charu C. Aggarwal

    Pages 443-467

20. Back Matter

    Pages 469-471

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This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

• Association rules
• Biometrics
• Data classification
• Data mining
• Data stream pattern mining
• Data streams
• Frequent pattern mining
• Frequent patterns
• Large itemsets
• Numerical data
• Pattern recognition
• Privacy preserving methods
• Sequential pattern mining
• Sequential patterns
• Vertical data representation

“This multiauthor volume offers a thorough review of methods in frequent pattern mining. … This volume will be an essential reference for both researchers and practitioners in data mining.” (H. Van Dyke Parunak, Computing Reviews, March, 2016)

• IBM, Yorktown Heights, USA

  Charu C. Aggarwal

• University of Illinois at Urbana-Champaign, Urbana, USA

  Jiawei Han

Charu Aggarwal is a Research Scientist at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. His research interest during his Ph.D. years was in combinatorial optimization (network flow algorithms), and his thesis advisor was Professor James B. Orlin. He has since worked in the field of data mining, with particular interests in data streams, privacy, uncertain data and social network analysis. He has published over 200 papers in refereed venues and has applied for or been granted over 80 patents. Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology and a recipient of an IBM Research Division Award (2008) for his scientific contributions to data stream research. He has served on the program committees of most major database/data mining conferences, and served as program vice-chairs of the SIAM Conference on Data Mining, 2007, the IEEE ICDM Conference, 2007, the WWW Conference 2009, and the IEEE ICDM Conference, 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal from 2004 to 2008. He is an associate editor of the ACM TKDD Journal an action editor of the Data Mining and Knowledge Discovery Journal , editor-in-chief of ACM SIGKDD Explorations and an associate editor of the Knowledge and Information Systems Journal. He is a fellow of the ACM (2013) and the IEEE (2010) for "contributions to knowledge discovery and data mining techniques". Jiawei Han received his BS from University of Science and Technology of China in 1979 and his PhD from the University of Wisconsin in Computer Science in 1985.He was a professor in the School of Computing Science at Simon Fraser University. Currently he is a professor, at the Department of Computer Science in the University of Illinois at Urbana-Champaign. He is also the Director of Information Network Academic Research Center (INARC) supported by Network Science Collaborative Technology Alliance (NSCTA) program of U.S. Army Research Lab (ARL). Han has chaired or served on over 100 program committees of international conferences and workshops, including PC co-chair of 2005 (IEEE), International Conference on Data Mining (ICDM), Americas Coordinator of 2006 International Conference on Very Large Data Bases (VLDB). He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM fellow and an IEEE Fellow. He received the 2004 ACM SIGKDD Innovations Award and the 2005 IEEE Computer Society Technical Achievement Award. The book: Han, Kamber and Pei,"Data Mining: Concepts and Techniques" (3rd ed., Morgan Kaufmann, 2011) has been popularly used as a textbook worldwide. He was the 2009 winner of the McDowell Award, the highest technical award made by IEEE. He teaches courses CS412 - Data Mining and CS512 - Advanced Data Mining at University of Illinois, Urbana Champaign. His course CS412 - Data Mining is highly popular among students and is over-subscribed in each offering.

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