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Initializing k-means Clustering by Bootstrap and Data Depth

Author

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  • Aurora Torrente

    (Universidad Carlos III de Madrid)

  • Juan Romo

    (Universidad Carlos III de Madrid)

Abstract

The k-means algorithm is widely used in various research fields because of its fast convergence to the cost function minima; however, it frequently gets stuck in local optima as it is sensitive to initial conditions. This paper explores a simple, computationally feasible method, which provides k-means with a set of initial seeds to cluster datasets of arbitrary dimensions. Our technique consists of two stages: firstly, we use the original data space to obtain a set of prototypes (cluster centers) by applying k-means to bootstrap replications of the data and, secondly, we cluster the space of centers, which has tighter (thus easier to separate) groups, and search the deepest point in each assembled cluster using a depth notion. We test this method with simulated and real data, compare it with commonly used k-means initialization algorithms, and show that it is feasible and more efficient than previous proposals in many situations.

Suggested Citation

  • Aurora Torrente & Juan Romo, 2021. "Initializing k-means Clustering by Bootstrap and Data Depth," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 232-256, July.
    • Handle: RePEc:spr:jclass:v:38:y:2021:i:2:d:10.1007_s00357-020-09372-3
    DOI: 10.1007/s00357-020-09372-3
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    References listed on IDEAS

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    Cited by:

    1. Giuseppe Pandolfo & Antonio D’ambrosio, 2023. "Clustering directional data through depth functions," Computational Statistics, Springer, vol. 38(3), pages 1487-1506, September.
    2. Javier Albert-Smet & Aurora Torrente & Juan Romo, 2023. "Band depth based initialization of K-means for functional data clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 463-484, June.

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