IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v047i14.html
   My bibliography  Save this article

bayesclust: An R Package for Testing and Searching for Significant Clusters

Author

Listed:
  • Gopal, Vikneswaran
  • Fuentes, Claudio
  • Casella, George

Abstract

The detection and determination of clusters has been of special interest among researchers from different fields for a long time. In particular, assessing whether the clusters are significant is a question that has been asked by a number of experimenters. In Fuentes and Casella (2009), the authors put forth a new methodology for analyzing clusters. It tests the hypothesis H0 : κ = 1 versus H1 : κ = k in a Bayesian setting, where κ denotes the number of clusters in a population. The bayesclust package implements this approach in R. Here we give an overview of the algorithm and a detailed description of the functions available in the package. The routines in bayesclust allow the user to test for the existence of clusters, and then pick out optimal partitionings of the data. We demonstrate the testing procedure with simulated datasets.

Suggested Citation

  • Gopal, Vikneswaran & Fuentes, Claudio & Casella, George, 2012. "bayesclust: An R Package for Testing and Searching for Significant Clusters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i14).
  • Handle: RePEc:jss:jstsof:v:047:i14
    DOI: http://hdl.handle.net/10.18637/jss.v047.i14
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v047i14/v47i14.pdf
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v047i14/bayesclust_3.1.tar.gz
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v047i14/v47i14.R
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v047.i14?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Gaynor, Sheila & Bair, Eric, 2017. "Identification of relevant subtypes via preweighted sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 139-154.
    13. Fischer, Aurélie, 2011. "On the number of groups in clustering," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1771-1781.
    14. 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.
    15. Osbert C Zalay, 2020. "Blind method for discovering number of clusters in multidimensional datasets by regression on linkage hierarchies generated from random data," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-28, January.
    16. 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.
    17. Jane L. Harvill & Priya Kohli & Nalini Ravishanker, 2017. "Clustering Nonlinear, Nonstationary Time Series Using BSLEX," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 935-955, September.
    18. Tomislava Pavić Kramarić & Mirjana Pejić Bach & Ksenija Dumičić & Berislav Žmuk & Maja Mihelja Žaja, 2018. "Exploratory study of insurance companies in selected post-transition countries: non-hierarchical cluster analysis," 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. 26(3), pages 783-807, September.
    19. Z. Volkovich & D. Toledano-Kitai & G.-W. Weber, 2013. "Self-learning K-means clustering: a global optimization approach," Journal of Global Optimization, Springer, vol. 56(2), pages 219-232, June.
    20. J. Fernando Vera & Rodrigo Macías, 2017. "Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 275-294, June.

    More about this item

    Statistics

    Access and download statistics

    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:jss:jstsof:v:047:i14. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    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.