Abstract
In this paper, we propose a method for mining Frequent Weighted Utility Itemsets (FWUIs) from quantitative databases. Firstly, we introduce the WIT (Weighted Itemset Tidset) tree data structure for mining high utility itemsets in the work of Le et al. (2009) and modify it into MWIT (M stands for Modification) tree for mining FWUIs. Next, we propose an algorithm for mining FWUIs using MWIT-tree. We test the proposed algorithm in many databases and show that they are very efficient.
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Vo, B., Le, B., Jung, J.J. (2012). A Tree-Based Approach for Mining Frequent Weighted Utility Itemsets. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_12
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DOI: https://doi.org/10.1007/978-3-642-34630-9_12
Publisher Name: Springer, Berlin, Heidelberg
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