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UGMINE: utility-based graph mining

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Abstract

Frequent pattern mining extracts most frequent patterns from databases. These frequency-based frameworks have limitations in representing users’ interest in many cases. In business decision-making, not all patterns are of the same importance. To solve this problem, utility has been incorporated in transactional and sequential databases. A graph is a relatively complex but highly useful data structure. Although frequency-based graph mining has many real-life applications, it has limitations similar to other frequency-based frameworks. To the best of our knowledge, there is no complete framework developed for mining utility-based patterns from graphs. In this work, we propose a complete framework for utility-based graph pattern mining. A complete algorithm named UGMINE is presented for high utility subgraph mining. We introduce a pruning technique named RMU pruning for effective pruning of the candidate pattern search space that grows exponentially. We conduct experiments on various datasets to analyze the performance of the algorithm. Our experimental results show the effectiveness of UGMINE to extract high utility subgraph patterns.

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Acknowledgements

We would like to express our deep gratitude to the anonymous reviewers of this article. We believe their useful comments have played a significant role in improving the quality of this work, which was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) and University of Manitoba.

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Authors and Affiliations

  1. University of Dhaka, Dhaka, Bangladesh

    Md. Tanvir Alam, Amit Roy, Chowdhury Farhan Ahmed & Md. Ashraful Islam

  2. University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung

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  1. Md. Tanvir Alam
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  2. Amit Roy
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  3. Chowdhury Farhan Ahmed
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  4. Md. Ashraful Islam
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  5. Carson K. Leung
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Correspondence to Chowdhury Farhan Ahmed.

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Alam, M.T., Roy, A., Ahmed, C.F. et al. UGMINE: utility-based graph mining. Appl Intell 53, 49–68 (2023). https://doi.org/10.1007/s10489-022-03385-8

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