DocumentCode :
2417848
Title :
APT-Structure: Efficient Mining of Frequent Patterns
Author :
Zhu, Shiwei ; Zhang, Renqian ; Xia, Guoping
Author_Institution :
Sch. of Econ. & Manage., Beihang Univ., Beijing, China
fYear :
2010
fDate :
7-9 May 2010
Firstpage :
1395
Lastpage :
1398
Abstract :
Frequent pattern mining is a key step in many data mining applications. In this paper, we propose a simple and novel pattern growth algorithm, which uses a compact data structure named Array-based Prefix Tree (APT). The APT has a distinct feature that the space requirement can be predictable in advance. The memory usage of APT is less than FP-Tree that uses pointer to maintain the link between parent and child nodes, and the traversal cost is lower. The mining algorithm based on APT uses top-down traversal strategy, and unfiltered pseudo-construct conditional database, which can improve computational performance. Further computational experiments show that APT algorithm is more efficient, and performs better than FPGrowth* and AFOPT.
Keywords :
data mining; data structures; trees (mathematics); AFOPT; APT-structure; FP-Tree; FPGrowth; array-based prefix tree; compact data structure; data mining; frequent pattern mining; pattern growth algorithm; top-down traversal strategy; unfiltered pseudo-construct conditional database; Arrays; Association rules; Itemsets; Prediction algorithms; Array-based Prefix Tree; Frequent pattern mining; association mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
E-Business and E-Government (ICEE), 2010 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3997-3
Type :
conf
DOI :
10.1109/ICEE.2010.354
Filename :
5591747
Link To Document :
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