Upcoming book: High Utility Itemset Mining: Theory, Algorithms and Applications

I am happy to announce that the draft of the book about high utility pattern mining has been finalized and submitted to the publisher (Springer). It should thus be published in the very near future.

high utility pattern mining

The book contains 12 chapters written by several top researchers from the field of pattern mining, for a total of 350 pages. The title is “High Utility Itemset Mining: Theory, Algorithms and Applications”. It discuss high utility itemset mining and other related topics. I show you here the table of content:

Editors: Philippe Fournier-Viger, Jerry Chun-Wei Lin, Bay Vo, Roger Nkambou, Vincent S. Tseng.

  • Chapter 1: A Survey of High Utility Itemset Mining
    Philippe Fournier-Viger, Jerry Chun-Wei Lin, Tin Truong Chi, Roger Nkambou
    This chapter gives a more than 39 pages introduction to high utility pattern mining, designed for getting a quick overview of the field and the main results.
  • Chapter 2: A Comparative Study of Top-K High Utility Itemset Mining Methods
    Srikumar Krishnamoorthy
    This chapter gives an in-depth discussion of top-k high utility itemset mining, including a very detailed comparison of the state-of-the-art algorithms.
  • Chapter 3: A Survey of High Utility Pattern Mining Algorithms for Big Data
    Morteza Zihayat, Methdi Kargar, Jaroslaw Szlichta
    This chapter reviews algorithms for mining high utility patterns in big data.
  • Chapter 4: A survey of High Utility Sequential Pattern Mining
    Tin Truong Chi, Philippe Fournier-Viger
    This chapter provides a survey of  high utility sequential pattern mining. It contains several new theoretical results and a very detailed comparison of upper-bounds and algorithms.
  • Chapter 5: Efficient Algorithms for High Utility Itemset Mining without Candidate Generation
    Jun-Feng Qu, Mengchi Liu, Philippe Fournier-Viger
    This chapter presents the HUI-Miner algorithm and a novel extension called HUI-Miner*, which improves is performance in many situations.
  • Chapter 6: High Utility Association Rule Mining
    Loan T.T. Nguyen, Thang Mai, Bay Vo
    This discusses another important topic of discovering high utility associations.
  • Chapter 7: Mining High-utility Irregular Itemsets
    Supachai Laoviboon, Komate Amphawan
    This chapter considers the time dimension in high utility itemset mining to find regular patterns.
  • Chapter 8: A survey of Privacy Preserving Utility Mining
    Duy-Tai Dinh, Van-Nam Huynh, Bac Le, Philippe Fournier-Viger, Ut Huynh, Quang-Minh Nguyen
    This chapter provides an overview of techniques for hiding high utility patterns for privacy purposes.
  • Chapter 9: Extracting Potentially High Profit Product Feature Groups by Using High Utility Pattern Mining and Aspect based Sentiment Analysis
    Seyfullah Demir, Oznur Alkan, Firat Cekinel, Pinar Karagoz
    This section presents an interesting application of high utility pattern mining related to sentiment analysis
  • Chapter 10: Metaheuristics for Frequent and High-Utility Itemset Mining
    Youcef Djenouri, Philippe Fournier-Viger, Asma Belhadi, Jerry Chun-Wei Lin
    This chapter provides a survey of evolutionary and swarm intelligence algorithms for high utility itemset mining.
  • Chapter 11: Mining Compact High Utility Itemsets without Candidate Generation
    Cheng-Wei Wu, Philippe Fournier-Viger, Jia-Yuan Gu, Vincent S. Tseng
    This chapter presents algorithms for mining closed and maximal high utility itemsets. It includes a novel strategy for identifying maximal patterns when using a depth-first search.
  • Chapter 12: Visualization and Visual Analytic Techniques for Patterns
    Wolfgang Jentner and Daniel A. Keim.
    This chapter discusses the problem of vizualizing patterns found.

This will be a very good book with many great contributions, and I am excited that it will be published soon. I will keep you updated on this blog as we get closer to the release.

Philippe Fournier-Viger is a professor, data mining researcher and the founder of the SPMF data mining software, which includes more than 100 algorithms for pattern mining.

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