Association Rule Mining III: Frequent Pattern Trees

  • Max Bramer
Part of the Undergraduate Topics in Computer Science book series (UTICS)


This chapter introduces the FP-growth algorithm for extracting frequent itemsets from a database of transactions. First the database is processed to produce a data structure called a FP-tree, then the tree is processed recursively by constructing a sequence of reduced trees known as conditional FP-trees, from which the frequent itemsets are extracted. The algorithm has the very desirable feature of requiring only two scans through the database.


  1. [1]
    Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. SIGMOD Record, 29(2), 1–12. Proceedings of the 2000 ACM SIGMOD international conference on management of data, ACM Press. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  • Max Bramer
    • 1
  1. 1.School of ComputingUniversity of PortsmouthPortsmouthUK

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