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Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

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Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

Mining high utility itemsets (HUIs) from databases is an important data mining task, which refers to the discovery of itemsets with high utilities (e.g. high profits). However, it may present too many HUIs to users, which also degrades the ef?ciency of the mining process. To achieve high efficiency for the mining task and provide a concise mining result to users, we propose a novel framework in this paper for mining closed high utility itemsets (CHUIs), which serves as a compact and lossless representation of HUIs. We propose three ef?cient algorithms named AprioriCH (Apriori-based algorithm for mining High utility Closed itemsets), AprioriHC-D (AprioriHC algorithm with Discarding unpromising and isolated items) and CHUD (Closed High Utility Itemset Discovery) to find this representation. Further, a method called DAHU (Derive All High Utility Itemsets) is proposed to recover all HUIs from the set of CHUIs without accessing the original database. Results on real and synthetic datasets show that the proposed algorithms are very efficient and that our approaches achieve a massive reduction in the number of HUIs. In addition, when all HUIs.



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  • Model: PROJ5215
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This product was added to our catalog on Friday 09 September, 2016.

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