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A Scalable Algorithm for Constructing Frequent Pattern Tree

Author

Listed:
  • Zailani Abdullah

    (Department of Computer Science, Universiti Malaysia, Terengganu, Malaysia)

  • Tutut Herawan

    (Department of Mathematics Education, Universitas Ahmad Dahlan, Yogyakarta, Indonesia)

  • A. Noraziah

    (Computer System and Software Engineering, Universiti Malaysia, Kuantan, Malaysia)

  • Mustafa Mat Deris

    (Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)

Abstract

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique Algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.

Suggested Citation

  • Zailani Abdullah & Tutut Herawan & A. Noraziah & Mustafa Mat Deris, 2014. "A Scalable Algorithm for Constructing Frequent Pattern Tree," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 10(1), pages 42-56, January.
    • Handle: RePEc:igg:jiit00:v:10:y:2014:i:1:p:42-56
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