Abstract
In the high-utility itemset mining (HUIM) model, the low-utility patterns sometimes with a very high-utility pattern will be considered as a valuable pattern even if this behavior may be not highly correlated. A more intelligent system that provides non-redundant and correlated behavior based on utility measure is desired. In this paper, we first present a novel method, called extracting non-redundant correlated purchase behaviors by utility measure, to determine the high qualified patterns, which can lead to higher recall and better precision. In the proposed projection-based approach, efficient projection mechanism and a sorted downward closure property are developed to reduce the database size. Two pruning strategies are further developed to efficiently and effectively discover the desired patterns. An extensive experimental study showed that the proposed algorithm considerably outperforms the existing HUIM algorithms.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61503092 and by the Tencent Project under grant CCF-Tencent IAGR20160115.
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Gan, W., Lin, J.CW., Fournier-Viger, P., Chao, HC. (2017). Extracting Non-redundant Correlated Purchase Behaviors by Utility Measure. In: Bellatreche, L., Chakravarthy, S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science(), vol 10440. Springer, Cham. https://doi.org/10.1007/978-3-319-64283-3_32
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DOI: https://doi.org/10.1007/978-3-319-64283-3_32
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