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NetHAPP: High Average Utility Periodic Gapped Sequential Pattern Mining

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Periodic Pattern Mining

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Abstract

Sequential pattern mining is a key data mining task, where the aim is to find subsequences appearing frequently in sequences of items (symbols). To provide more flexibility and reveal more valuable patterns, sequential pattern mining with a periodic gap has emerged as an important extension. Algorithms for this task identify repetitive gapped subsequences (patterns) in a sequence. Although this has many applications, patterns are only selected based on their occurrence frequency and the external utility (relative importance) of each symbol is ignored. Consequently, these methods can find many unimportant frequent patterns and neglect some low frequency but extremely important patterns. To address this problem, this chapter presents a novel task of High Average Utility Periodic Gapped Sequential Pattern (HAPP) mining and proposes an efficient algorithm called Nettree for HAPP (NetHAPP), which involves two key steps: support calculation and candidate pattern generation. To calculate the support of patterns, a backtracking strategy is adopted that effectively reduces the time complexity of algorithm. To reduce the number of candidate patterns, an average utility upper bound method is combined with a pattern join strategy. A wide range of experimental results show that NetHAPP is not only more efficient than competitive algorithms but can also discover more valuable patterns.

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Acknowledgements

This work was partly supported by National Natural Science Foundation of China (61976240, 52077056), and Graduate Student Innovation Program of Hebei Province (CXZZBS2020024).

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Youxi Wu

  2. Hebei Key Laboratory of Big Data Computing, Tianjin, 300401, China

    Youxi Wu

  3. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Meng Geng

  4. School of Economics and Management, Hebei University of Technology, Tianjin, 300401, China

    Yan Li

  5. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300401, China

    Lei Guo

  6. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

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  1. Youxi Wu
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  2. Meng Geng
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  3. Yan Li
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  4. Lei Guo
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  5. Philippe Fournier-Viger
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Correspondence to Philippe Fournier-Viger .

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Editors and Affiliations

  1. Division of Information Systems, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    R. Uday Kiran

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Jose M. Luna

  4. Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  5. Department of Computer Science, Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

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Wu, Y., Geng, M., Li, Y., Guo, L., Fournier-Viger, P. (2021). NetHAPP: High Average Utility Periodic Gapped Sequential Pattern Mining. In: Kiran, R.U., Fournier-Viger, P., Luna, J.M., Lin, J.CW., Mondal, A. (eds) Periodic Pattern Mining . Springer, Singapore. https://doi.org/10.1007/978-981-16-3964-7_11

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