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IDEAL
2003
Springer

Experiences of Using a Quantitative Approach for Mining Association Rules

9 years 11 months ago
Experiences of Using a Quantitative Approach for Mining Association Rules
In recent years interest has grown in “mining” large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an emerging research area. Association rules discovery is an important KDD technique for better data understanding. This paper proposes an enhancement with a memory efficient data structure of a quantitative approach to mine association rules from data. The best features of the three algorithms (the Quantitative Approach, DHP, and Apriori) were combined to constitute our proposed approach. The obtained results accurately reflected knowledge hidden in the datasets under examination. Scale-up experiments indicated that the proposed algorithm scales linearly as the size of the dataset increases.
L. Dong, Christos Tjortjis
Added 07 Jul 2010
Updated 07 Jul 2010
Type Conference
Year 2003
Where IDEAL
Authors L. Dong, Christos Tjortjis
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