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Finite Libby-Novick Beta Mixture Model: An MML-Based Approach

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Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13995))

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Abstract

We propose an unsupervised algorithm for learning the optimal number of clusters in a finite Libby-Novick Beta mixture model. In unsupervised learning, it is crucial to determine the number of clusters that best describes the data. By extending the minimum message length (MML) principle, we are able to determine the number of clusters in Libby-Novick Beta mixtures. Our model has been evaluated on three publicly available and real-world medical datasets.

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Acknowledgment

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC).

Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada

    Niloufar Samiee, Narges Manouchehri & Nizar Bouguila

  2. Algorithmic Dynamics Lab, Unit of Computational Medicine, Karolinska Institute, Stockholm, Sweden

    Narges Manouchehri

Authors
  1. Niloufar Samiee
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  2. Narges Manouchehri
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  3. Nizar Bouguila
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Corresponding author

Correspondence to Niloufar Samiee .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University Iwate, Iwate, Japan

    Hamido Fujita

  4. Wroclaw University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Samiee, N., Manouchehri, N., Bouguila, N. (2023). Finite Libby-Novick Beta Mixture Model: An MML-Based Approach. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_30

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  • DOI: https://doi.org/10.1007/978-981-99-5834-4_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5833-7

  • Online ISBN: 978-981-99-5834-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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