mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets

  • Alex Yuxuan Peng
  • Yun Sing Koh
  • Patricia Riddle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

High utility itemset mining is the problem of finding sets of items whose utilities are higher than or equal to a specific threshold. We propose a novel technique called mHUIMiner, which utilises a tree structure to guide the itemset expansion process to avoid considering itemsets that are nonexistent in the database. Unlike current techniques, it does not have a complex pruning strategy that requires expensive computation overhead. Extensive experiments have been done to compare mHUIMiner to other state-of-the-art algorithms. The experimental results show that our technique outperforms the state-of-the-art algorithms in terms of running time for sparse datasets.

Keywords

High-utility itemset mining Transaction utility 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alex Yuxuan Peng
    • 1
  • Yun Sing Koh
    • 1
  • Patricia Riddle
    • 1
  1. 1.The University of AucklandAucklandNew Zealand

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