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An Adaptive Strategy for Dynamic Data Clustering with the K-Means Algorithm

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Parallel Processing and Applied Mathematics (PPAM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12044))

Abstract

K-means algorithm is one of the most widely used methods in data mining and statistical data analysis to partition several objects in K distinct groups, called clusters, on the basis of their similarities. The main problem of this algorithm is that it requires the number of clusters as an input data, but in the real life it is very difficult to fix in advance such value. For such reason, several modified K-means algorithms are proposed where the number of clusters is defined at run time, increasing it in a iterative procedure until a given cluster quality metric is satisfied. In order to face the high computational cost of this approach we propose an adaptive procedure, where at each iteration two new clusters are created, splitting only the one with the worst value of the quality metric.

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Authors and Affiliations

  1. Department of Mathematics and Applications, University of Naples Federico II, Naples, Italy

    Marco Lapegna & Valeria Mele

  2. Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Naples, Italy

    Diego Romano

Authors
  1. Marco Lapegna
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  2. Valeria Mele
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  3. Diego Romano
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Corresponding author

Correspondence to Marco Lapegna .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Southern California, Marina del Rey, CA, USA

    Ewa Deelman

  3. University of Tennessee, Knoxville, TN, USA

    Jack Dongarra

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Lapegna, M., Mele, V., Romano, D. (2020). An Adaptive Strategy for Dynamic Data Clustering with the K-Means Algorithm. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2019. Lecture Notes in Computer Science(), vol 12044. Springer, Cham. https://doi.org/10.1007/978-3-030-43222-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-43222-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43221-8

  • Online ISBN: 978-3-030-43222-5

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