Abstract
High utility itemset mining (HUIM) aims at knowledge discovery from the datasets by finding patterns that have high utility values. Most of the existing algorithms suffer from the drawback of generating huge number of results that overwhelm the decision-making process for industry applications. Also, the real-life datasets often consist of items that have both positive and negative utility values in order to represent the profit and losses, respectively. In this paper, we propose a novel mining algorithm that maps closely to the real-life applications by producing only a reasonable number of outputs based on a support measure, from the datasets that have both positive and negative utility values. Several experiments are undertaken to test the efficacy of the proposed approach. Empirical evaluation suggests that the proposed approach is highly efficient for dense datasets.
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Pushp, Chand, S. (2022). Support-Based High Utility Mining with Negative Utility Values. In: Bashir, A.K., Fortino, G., Khanna, A., Gupta, D. (eds) Proceedings of International Conference on Computing and Communication Networks. Lecture Notes in Networks and Systems, vol 394. Springer, Singapore. https://doi.org/10.1007/978-981-19-0604-6_18
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DOI: https://doi.org/10.1007/978-981-19-0604-6_18
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