Abstract
Association rule is a kind of important knowledge extracted from databases. However, a large number of association rules may be extracted. It is difficult for a user to understand them. How to select some “representative” rules is thus an important and interesting topic. In this paper, we proposed a distance-based approach as a post-processing for association rules on numeric attributes. Our approach consists of two phases. First, a heuristic algorithm is used to cluster rules based on a matrix of which element is the distance of two rules. Second, after clustering, we select a representative rule for each cluster based on an objective measure. We applied our approach to a real database. As the result, three representative rules are selected, instead of more than 300 original association rules.
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Du, X., Suzuki, S., Ishii, N. (1999). A Distance-Based Clustering and Selection of Association Rules on Numeric Attributes. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_51
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DOI: https://doi.org/10.1007/978-3-540-48061-7_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
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