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Positive Correlation Based Efficient High Utility Pattern Mining Approach

Part of the Communications in Computer and Information Science book series (CCIS,volume 1483)


The problem of high utility pattern (HUP) mining is an interesting task and comes with various applications. It has been argued in various past studies that the patterns with strong mutual correlation are more useful for decision making. A more powerful tool that takes the inherent correlation into account is more desirable. This paper presents an Efficient Correlated High Utility Miner (ECHUM) that considers the positive correlation among the items to find correlated high utility itemsets. The ECHUM uses a list structure is to avoid multiple scanning of the dataset. The search space is significantly reduced by using two upper-bounds named sub-tree utility and the local utility. Also, several database projection methods and transaction merging methods are used to reduce the complexity. Several pruning strategies are adopted to make the algorithm fast and memory efficient. A variety of experiments conducted shows that ECHUM is 2–3 times faster, and the memory usage is also 3–4 times lesser than the existing algorithm.


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Correspondence to Dharavath Ramesh .

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Ramesh, D., Sethi, K.K., Rathore, A. (2021). Positive Correlation Based Efficient High Utility Pattern Mining Approach. In: Venugopal, K.R., Shenoy, P.D., Buyya, R., Patnaik, L.M., Iyengar, S.S. (eds) Data Science and Computational Intelligence. ICInPro 2021. Communications in Computer and Information Science, vol 1483. Springer, Cham.

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  • Print ISBN: 978-3-030-91243-7

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