Kernel-Tree: Mining Frequent Patterns in a Data Stream Based on Forecast Support

  • David Tse Jung Huang
  • Yun Sing Koh
  • Gillian Dobbie
  • Russel Pears
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7691)


Although frequent pattern mining techniques have been extensively studied, the extension of their application onto data streams has been challenging. Due to data streams being continuous and unbounded, an efficient algorithm that avoids multiple scans of data is needed. In this paper we propose Kernel-Tree (KerTree), a single pass tree structured technique that mines frequent patterns in a data stream based on forecasting the support of current items in the future state. Unlike previous techniques that build a tree based on the support of items in the previous block, KerTree performs an estimation of item support in the next block and builds the tree based on the estimation. By building the tree on an estimated future state, KerTree effectively reduces the need to restructure for every block and thus results in a better performance and mines the complete set of frequent patterns from the stream while maintaining a compact structure.


Data Streams Kernel Regression Frequent Pattern Mining 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David Tse Jung Huang
    • 1
  • Yun Sing Koh
    • 1
  • Gillian Dobbie
    • 1
  • Russel Pears
    • 2
  1. 1.Department of Computer ScienceUniversity of AucklandNew Zealand
  2. 2.School of Computing and Mathematical SciencesAUT UniversityNew Zealand

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