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The Concept of a New Neural Map for Clustering, Data Visualization and Prediction with Probability Distribution Approximation

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Information Systems (EMCIS 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 464))

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Abstract

The paper submits a proposition of a new data analysis tool (named PDCM – Probability Distribution generating and Clustering Maps) constructed in the form of an extended neural map and dedicated to a variety of tasks, such as specific clustering, visualization, prediction (together with its possible visual analysis and justification) and generation of approximated Bayesian a posteriori probability density distribution for dependent variable. Basic theoretical aspects concerning the structure and training process of the proposed model have been presented. Also, research involving application of the PDCM method for real estate market data (Boston dataset) has been shown together with promising research results and conclusions.

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Acknowledgements

The publication is financed by the Krakow University of Economics under the program “Support for Conference Activity - WAK-2022”.

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Authors and Affiliations

  1. Krakow University of Economics, Krakow, Poland

    Janusz Morajda

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  1. Janusz Morajda
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Correspondence to Janusz Morajda .

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Editors and Affiliations

  1. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  2. University of Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

  3. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  4. University of Nicosia, Nicosia, Cyprus

    Klitos Christodoulou

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Morajda, J. (2023). The Concept of a New Neural Map for Clustering, Data Visualization and Prediction with Probability Distribution Approximation. In: Papadaki, M., Rupino da Cunha, P., Themistocleous, M., Christodoulou, K. (eds) Information Systems. EMCIS 2022. Lecture Notes in Business Information Processing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-031-30694-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-30694-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30693-8

  • Online ISBN: 978-3-031-30694-5

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