IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v35y2020i4d10.1007_s00180-020-00981-5.html
   My bibliography  Save this article

Estimating the number of clusters via a corrected clustering instability

Author

Listed:
  • Jonas M. B. Haslbeck

    (University of Amsterdam)

  • Dirk U. Wulff

    (University of Basel
    Max Planck Institute for Human Development)

Abstract

We improve instability-based methods for the selection of the number of clusters k in cluster analysis by developing a corrected clustering distance that corrects for the unwanted influence of the distribution of cluster sizes on cluster instability. We show that our corrected instability measure outperforms current instability-based measures across the whole sequence of possible k, overcoming limitations of current insability-based methods for large k. We also compare, for the first time, model-based and model-free approaches to determining cluster-instability and find their performance to be comparable. We make our method available in the R-package cstab.

Suggested Citation

  • Jonas M. B. Haslbeck & Dirk U. Wulff, 2020. "Estimating the number of clusters via a corrected clustering instability," Computational Statistics, Springer, vol. 35(4), pages 1879-1894, December.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:4:d:10.1007_s00180-020-00981-5
    DOI: 10.1007/s00180-020-00981-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-020-00981-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-020-00981-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    2. Fujita, André & Takahashi, Daniel Y. & Patriota, Alexandre G., 2014. "A non-parametric method to estimate the number of clusters," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 27-39.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    4. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    5. Fang, Yixin & Wang, Junhui, 2012. "Selection of the number of clusters via the bootstrap method," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 468-477.
    6. Junhui Wang, 2010. "Consistent selection of the number of clusters via crossvalidation," Biometrika, Biometrika Trust, vol. 97(4), pages 893-904.
    7. Yoshua Bengio & Pascal Vincent & Jean-François Paiement, 2003. "Spectral Clustering and Kernel PCA are Learning Eigenfunctions," CIRANO Working Papers 2003s-19, CIRANO.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Olivia Fischer & Loris T. Jeitziner & Dirk U. Wulff, 2024. "Affect in science communication: a data-driven analysis of TED Talks on YouTube," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Radchenko & Gourab Mukherjee, 2017. "Convex clustering via l 1 fusion penalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1527-1546, November.
    2. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
    3. Han Yu & Brian Chapman & Arianna Di Florio & Ellen Eischen & David Gotz & Mathews Jacob & Rachael Hageman Blair, 2019. "Bootstrapping estimates of stability for clusters, observations and model selection," Computational Statistics, Springer, vol. 34(1), pages 349-372, March.
    4. Julian Rossbroich & Jeffrey Durieux & Tom F. Wilderjans, 2022. "Model Selection Strategies for Determining the Optimal Number of Overlapping Clusters in Additive Overlapping Partitional Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 264-301, July.
    5. Fang, Yixin & Wang, Junhui, 2012. "Selection of the number of clusters via the bootstrap method," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 468-477.
    6. Koltcov, Sergei, 2018. "Application of Rényi and Tsallis entropies to topic modeling optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1192-1204.
    7. Paul, Biplab & De, Shyamal K. & Ghosh, Anil K., 2022. "Some clustering-based exact distribution-free k-sample tests applicable to high dimension, low sample size data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    8. Fujita, André & Takahashi, Daniel Y. & Patriota, Alexandre G., 2014. "A non-parametric method to estimate the number of clusters," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 27-39.
    9. Vincent Audigier & Ndèye Niang, 2023. "Clustering with missing data: which equivalent for Rubin’s rules?," 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(3), pages 623-657, September.
    10. Lim, Alejandro & Chiang, Chin-Tsang & Teng, Jen-Chieh, 2021. "Estimating robot strengths with application to selection of alliance members in FIRST robotics competitions," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    11. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    12. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    13. Zhang, Tonglin & Lin, Ge, 2021. "Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    14. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    15. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    16. Fang, Yixin & Wang, Junhui, 2011. "Penalized cluster analysis with applications to family data," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2128-2136, June.
    17. Athanasios Constantopoulos & John Yfantopoulos & Panos Xenos & Athanassios Vozikis, 2019. "Cluster shifts based on healthcare factors: The case of Greece in an OECD background 2009-2014," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 9(6), pages 1-4.
    18. Yi Peng & Yong Zhang & Gang Kou & Yong Shi, 2012. "A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    19. Z. Volkovich & Z. Barzily & G.-W. Weber & D. Toledano-Kitai & R. Avros, 2012. "An application of the minimal spanning tree approach to the cluster stability problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 119-139, March.
    20. Coraggio, Luca & Coretto, Pietro, 2023. "Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score," Journal of Multivariate Analysis, Elsevier, vol. 196(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:35:y:2020:i:4:d:10.1007_s00180-020-00981-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.