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A Scalable Algorithm for Constructing Frequent Pattern Tree

A Scalable Algorithm for Constructing Frequent Pattern Tree

Zailani Abdullah, Tutut Herawan, A. Noraziah, Mustafa Mat Deris
Copyright: © 2014 |Volume: 10 |Issue: 1 |Pages: 15
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781466654792|DOI: 10.4018/ijiit.2014010103
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MLA

Abdullah, Zailani, et al. "A Scalable Algorithm for Constructing Frequent Pattern Tree." IJIIT vol.10, no.1 2014: pp.42-56. http://doi.org/10.4018/ijiit.2014010103

APA

Abdullah, Z., Herawan, T., Noraziah, A., & Deris, M. M. (2014). A Scalable Algorithm for Constructing Frequent Pattern Tree. International Journal of Intelligent Information Technologies (IJIIT), 10(1), 42-56. http://doi.org/10.4018/ijiit.2014010103

Chicago

Abdullah, Zailani, et al. "A Scalable Algorithm for Constructing Frequent Pattern Tree," International Journal of Intelligent Information Technologies (IJIIT) 10, no.1: 42-56. http://doi.org/10.4018/ijiit.2014010103

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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.

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