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An optimized FP-growth algorithm for discovery of association rules

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Abstract

Association rule mining (ARM) is a data mining technique to discover interesting associations between datasets. The frequent pattern-growth (FP-growth) is an effective ARM algorithm for compressing information in the tree structure. However, it tends to suffer from the performance gap when processing large databases because of its mining procedure. This study presents a modified FP-growth (MFP-growth) algorithm to enhance the efficiency of the FP-growth by obviating the need for recurrent creation of conditional subtrees. The proposed algorithm uses a header table configuration to reduce the complexity of the whole frequent pattern tree. Four experimental series are conducted using different benchmark datasets to analyze the operating efficiency of the proposed MFP-growth algorithm compared with state-of-the-art machine learning algorithms in terms of runtime, memory consumption, and the effectiveness of generated rules. The experimental results confirm the superiority of the MFP-growth algorithm, which focuses on its potential implementations in various contexts.

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Authors and Affiliations

  1. Communications and Information Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Mai Shawkat

  2. Department of Computer Science and Informatics, Taibah University, Medina, Saudi Arabia

    Mahmoud Badawi

  3. Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Mahmoud Badawi & Ali El-desoky

  4. Computer Engineering Department, MISR Higher Institute for Engineering and Technology, Scientific Research Group in Egypt (SRGE), Mansoura, Egypt

    Sally El-ghamrawy

  5. Computer and Systems Engineering Department, Delta Higher Institute for Engineering and Technology (DHIET), Mansoura, Egypt

    Reham Arnous

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  1. Mai Shawkat
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  2. Mahmoud Badawi
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  3. Sally El-ghamrawy
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  4. Reham Arnous
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  5. Ali El-desoky
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Correspondence to Sally El-ghamrawy.

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Shawkat, M., Badawi, M., El-ghamrawy, S. et al. An optimized FP-growth algorithm for discovery of association rules. J Supercomput 78, 5479–5506 (2022). https://doi.org/10.1007/s11227-021-04066-y

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