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The New K-Means Initialization Method

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Computational Collective Intelligence (ICCCI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14810))

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Abstract

Data clustering methods are crucial in various data analysis and machine learning fields. They are essential for organizing and understanding complex datasets by grouping similar (according to the assumed distance function) data. These methods facilitate customer segmentation, anomaly detection, pattern recognition and many other practical problems. Various clustering algorithms can be found in the literature, but the most popular method is the k-means algorithm and its subsequent modifications. This work presents a new method of initializing the k-means algorithm, which is tested on random and benchmark datasets. This paper discusses the importance of selecting the appropriate initialization of the k-means clustering method, demonstrating the effectiveness and superiority of the developed method.

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Authors and Affiliations

  1. Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Bartosz Brejna, Marcin Pietranik & Adrianna Kozierkiewicz

Authors
  1. Bartosz Brejna
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  2. Marcin Pietranik
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  3. Adrianna Kozierkiewicz
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Corresponding author

Correspondence to Marcin Pietranik .

Editor information

Editors and Affiliations

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

    Ngoc Thanh Nguyen

  2. University of Leipzig, Leipzig, Germany

    Bogdan Franczyk

  3. University of Leipzig, Leipzig, Sachsen, Germany

    André Ludwig

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  5. Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

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

    Adrianna Kozierkiewicz

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Brejna, B., Pietranik, M., Kozierkiewicz, A. (2024). The New K-Means Initialization Method. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14810. Springer, Cham. https://doi.org/10.1007/978-3-031-70816-9_29

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  • DOI: https://doi.org/10.1007/978-3-031-70816-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70815-2

  • Online ISBN: 978-3-031-70816-9

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