Frequent Itemset Mining in High Dimensional Data: A Review

  • Fatimah Audah Md. ZakiEmail author
  • Nurul Fariza Zulkurnain
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 481)


This paper provides a brief overview of the techniques used in frequent itemset mining. It discusses the search strategies used; i.e. depth first vs. breadth-first, and dataset representation; i.e. horizontal vs. vertical representation. In addition, it reviews many techniques used in several algorithms that make frequent itemset mining more efficient. These algorithms are discussed based on the proposed search strategies which include row-enumeration vs. column-enumeration, bottom-up vs. top-down traversal, and a number of new data structures. Finally, the paper reviews on the latest algorithms of colossal frequent itemset/pattern which currently is the most relevant to mining high-dimensional dataset.


Data mining High-dimensional data 


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The authors would like to thank the Malaysian Government and International Islamic University Malaysia (IIUM) for the research grant under Fundamental Research Grant Scheme (FRGS) Research Project FRGS14-139-0380.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Fatimah Audah Md. Zaki
    • 1
    Email author
  • Nurul Fariza Zulkurnain
    • 1
  1. 1.Department of Electrical and Computer EngineeringInternational Islamic University MalaysiaKuala LumpurMalaysia

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