A Scalable Algorithm for Constructing Frequent Pattern Tree

Zailani Abdullah, Tutut Herawan, A. Noraziah and Mustafa Mat Deris
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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

International Journal of Intelligent Information Technologies (IJIIT), 2014, vol. 10, issue 1, 42-56

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.

Date: 2014
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