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. © Springer-Verlag 2003.
CITATION STYLE
Dong, L., & Tjortjis, C. (2004). Experiences of using a quantitative approach for mining association rules. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2690, 693–700. https://doi.org/10.1007/978-3-540-45080-1_93
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