Abstract
The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.
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Lin, CW., Hong, TP., Lu, WH. (2010). Efficiently Mining High Average Utility Itemsets with a Tree Structure. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12145-6_14
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DOI: https://doi.org/10.1007/978-3-642-12145-6_14
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