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Mining top-k frequent patterns from uncertain databases

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Abstract

Mining uncertain frequent patterns (UFPs) from uncertain databases was recently introduced, and there are various approaches to solve this problem in the last decade. However, systems are often faced with the problem of too many UFPs being discovered by the traditional approaches to this issue, and thus will spend a lot of time and resources to rank and find the most promising patterns. Therefore, this paper introduces a task named mining top-k UFPs from uncertain databases. We then propose an efficient method named TUFP (mining Top-k UFPs) to carry this out. Effective threshold raising strategies are introduced to help the proposed algorithm reduce the number of generated candidates to enhance the performance in terms of the runtime as well as memory usage. Finally, several experiments on the number of generated candidates, mining time, memory usage and scalability of TUFP and two state-of-the-art approaches (CUFP-mine and LUNA) were conducted. The performance studies show that TUFP is efficient in terms of mining time, memory usage and scalability for mining top-k UFPs.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Tuong Le

  2. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Bay Vo

  3. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Van-Nam Huynh

  4. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  5. Digital Contents Research Institute, Sejong University, Seoul, Republic of Korea

    Sung Wook Baik

Authors
  1. Tuong Le
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  2. Bay Vo
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  3. Van-Nam Huynh
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  4. Ngoc Thanh Nguyen
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  5. Sung Wook Baik
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Correspondence to Bay Vo.

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Le, T., Vo, B., Huynh, VN. et al. Mining top-k frequent patterns from uncertain databases. Appl Intell 50, 1487–1497 (2020). https://doi.org/10.1007/s10489-019-01622-1

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