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Mining significant least association rules using fast SLP-growth algorithm

Abdullah, Zailani and Herawan, Tutut and Mat Deris, Mustafa (2010) Mining significant least association rules using fast SLP-growth algorithm. In: AST/UCMA/ISA/ACN'10: Proceedings of the 2010 international conference on Advances in Computer Science and Information Technology.

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Development of least association rules mining algorithms are very challenging in data mining. The complexity and excessive in computational cost are always become the main obstacles as compared to mining the frequent rules. Indeed, most of the previous studies still adopting the Apriori-like algorithms which are very time consuming. To address this issue, this paper proposes a scalable trie-based algorithm named SLP-Growth. This algorithm generates the significant patterns using interval support and determines its correlation. Experiments with the real datasets show that the SLP-algorithm can discover highly positive correlated and significant of least association. Indeed, it also outperforms the fast FP-Growth algorithm up to two times, thus verifying its efficiency.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: correlated; data mining; least association rules
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 23 Apr 2015 00:37
Last Modified: 23 Apr 2015 00:37
URI: http://eprints.uthm.edu.my/id/eprint/3588
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