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Mining Correlated High-Utility Itemsets Using the Bond Measure

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Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

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Abstract

Mining high-utility itemsets (HUIs) is the task of finding the sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding many itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in high-utility itemset mining to find profitable itemsets that are highly correlated, using the bond measure. An efficient algorithm named FCHM (Fast Correlated high-utility itemset Miner) is proposed to efficiently discover correlated high-utility itemsets. Experimental results show that FCHM is highly-efficient and can prune a huge amount of weakly correlated HUIs.

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Acknowledgement

This research was partially supported by National Natural Science Foundation of China (NSFC) under grant No.61503092.

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Correspondence to Philippe Fournier-Viger .

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Fournier-Viger, P., Lin, J.CW., Dinh, T., Le, H.B. (2016). Mining Correlated High-Utility Itemsets Using the Bond Measure. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

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