Proceedings of the 2012 National Conference on Information Technology and Computer Science

SeqARM: An Association Rule Mining Algorithm Based on Sequence Constraint

Authors
Shenshen Bai
Corresponding Author
Shenshen Bai
Available Online November 2012.
DOI
https://doi.org/10.2991/citcs.2012.204How to use a DOI?
Keywords
association rule; sequence constraint; SeqARM; FITree
Abstract
Sequence constraint mainly considers the occurrence time of item, which can determine whether an association rule is valid. This paper proposes a novel algorithm SeqARM that aims to mine strong association rule with sequence constraint and runs on the second phase of association rule mining. SeqARM employs a fine data structure, named FI-Tree, which is used to save and find frequent itemsets according to a few characteristics of association rule. This work can dramatically reduce the number of invalid association rules, and speed up the procedure of association rules. At last, the experiments prove that SeqARM can improve the performance and effect of the association rule mining.
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Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.204How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shenshen Bai
PY  - 2012/11
DA  - 2012/11
TI  - SeqARM: An Association Rule Mining Algorithm Based on Sequence Constraint
BT  - 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 804
EP  - 807
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.204
DO  - https://doi.org/10.2991/citcs.2012.204
ID  - Bai2012/11
ER  -