Abstract
Two important constraints of association rule mining algorithm are support and confidence. However, such constraints-based algorithms generally produce a large number of redundant rules. In many cases, if not all, number of redundant rules is larger than number of essential rules, consequently the novel intention behind association rule mining becomes vague. To retain the goal of association rule mining, we present several methods to eliminate redundant rules and to produce small number of rules from any given frequent or frequent closed itemsets generated. The experimental evaluation shows that the proposed methods eliminate significant number of redundant rules.
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© 2004 Springer-Verlag Berlin Heidelberg
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Ashrafi, M.Z., Taniar, D., Smith, K. (2004). A New Approach of Eliminating Redundant Association Rules. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2004. Lecture Notes in Computer Science, vol 3180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30075-5_45
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DOI: https://doi.org/10.1007/978-3-540-30075-5_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22936-0
Online ISBN: 978-3-540-30075-5
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