Abstract
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.
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Dong, L., Tjortjis, C. (2003). Experiences of Using a Quantitative Approach for Mining Association Rules. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_93
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DOI: https://doi.org/10.1007/978-3-540-45080-1_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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