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MaxFEM: Mining Maximal Frequent Episodes in Complex Event Sequences

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13651))

Abstract

For the analysis of discrete sequences, frequent episode mining (FEM) is a key technique. The goal is to enumerate all subsequences of symbols or events that are appearing at least some minimum number of times. In the last decades, several efficient episode mining algorithms were designed. Nonetheless, a major issue is that they often yield a huge number of frequent episodes, which is inconvenient for users. As a solution, this paper presents an efficient algorithm called MaxFEM (Maximal Frequent Episode Miner) to identify only the maximal frequent episodes of a complex sequence. A major benefit is to reduce the set of frequent episodes presented to the user. MaxFEM includes many strategies to improve its performance. The evaluation of MaxFEM on real datasets confirms that it has excellent performance.

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Author information

Authors and Affiliations

  1. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger, M. Saqib Nawaz & Yulin He

  2. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China

    Yulin He

  3. Hebei University of Technology, Tianjin, China

    Youxi Wu

  4. University of Bordj Bou Arreridj, El Anceur, Algeria

    Farid Nouioua

  5. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Unil Yun

Authors
  1. Philippe Fournier-Viger
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  2. M. Saqib Nawaz
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  3. Yulin He
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  4. Youxi Wu
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  5. Farid Nouioua
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  6. Unil Yun
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Corresponding author

Correspondence to M. Saqib Nawaz .

Editor information

Editors and Affiliations

  1. Mahasarakham University, Maha Sarakham, Thailand

    Olarik Surinta

  2. Hong Kong Polytechnic University, Hong Kong, China

    Kevin Kam Fung Yuen

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Fournier-Viger, P., Nawaz, M.S., He, Y., Wu, Y., Nouioua, F., Yun, U. (2022). MaxFEM: Mining Maximal Frequent Episodes in Complex Event Sequences. In: Surinta, O., Kam Fung Yuen, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2022. Lecture Notes in Computer Science(), vol 13651. Springer, Cham. https://doi.org/10.1007/978-3-031-20992-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-20992-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20991-8

  • Online ISBN: 978-3-031-20992-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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