Abstract
To avoid the loss of semantic information due to the partition of quantitative values, this paper proposes a novel algorithm, called MPSQAR, to handle the quantitative association rules mining. And the main contributions include: (1) propose a new method to normalize the quantitative values; (2) assign a weight for each attribute to reflect the values distribution; (3) extend the weight-based association model to tackle the quantitative values in association rules without partition; (4) propose a uniform method to mine the traditional binary association rules and quantitative association rules; (5) show the effectiveness and scalability of new method by experiments.
This work was supported by NSFC Grants (60773169 and 90409007), 11-th Five Years Key Programs for Sci. &Tech. Development of China under grant No. 2006BAI05A01.
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Zeng, C. et al. (2008). MPSQAR: Mining Quantitative Association Rules Preserving Semantics. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_58
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DOI: https://doi.org/10.1007/978-3-540-88192-6_58
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