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Covariance Controlled Bayesian Rose Trees

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

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Abstract

This paper aims to present a modified version of Bayesian Rose Trees (BRT). The classical BRT approach performs data clustering without restricting the resulting hierarchy to the binary tree. The proposed method allows for constraining the resulting hierarchies on the basis of additional knowledge. Thanks to this modification, it is possible to analyse not only the raw structure of the data but also the nature of a cluster. This allows an automatic interpretation of the resulting hierarchies while differentiating between clusters of different magnitudes, or those that extend significantly through one pair of dimensions while being coherent in a different one. On the basis of the resulting modifications, it is possible to analyse the depth level as a function of likelihood. The developed method allows maximising customisation possibilities and comparative analysis between the nature of clusters. It can be applied to the clustering of different types of content, e.g. visual, textual, or in a modern approach to the construction of container databases.

Supported by organization Infotower sp. z o.o., Wincentego Pola 16, 44-100 Gliwice, Poland.

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Acknowledgements

The research presented in this paper is co-financed by the EU Smart Growth Operational Programme 2014-2020 under the project POIR.01.01.01-00-1111/19 “Development of the advanced algorithms for multimedia data selection and the innovative method of those data visualization for the platform supporting customer service in the tourism industry”.

The work of Eryka Probierz was supported in part by the European Union through the European Social Fund as a scholarship Grant POWR.03.02.00-00-I029, and in part by the Silesian University of Technology through a grant: the subsidy for maintaining and developing the research potential in 2022 for young researchers in data collection and analysis.

The work of Damian Pęszor was supported in part by Silesian University of Technology through a grant number BKM-647/RAU6/2021 “Detection of a plane in stereovision images without explicit estimation of disparity with the use of correlation space”.

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Authors and Affiliations

  1. Silesian University of Technology, Akademicka 2A, 44-100, Gliwice, Poland

    Damian Pęszor & Eryka Probierz

  2. Infotower sp. z o.o., Wincentego Pola 16, 44-100, Gliwice, Poland

    Damian Pęszor & Eryka Probierz

Authors
  1. Damian Pęszor
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  2. Eryka Probierz
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Correspondence to Damian Pęszor .

Editor information

Editors and Affiliations

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

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Pęszor, D., Probierz, E. (2022). Covariance Controlled Bayesian Rose Trees. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_5

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  • DOI: https://doi.org/10.1007/978-3-031-21967-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

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