IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v43y2026i1d10.1007_s00357-025-09522-5.html

Implications of Different Encodings of Binned Data when Clustering

Author

Listed:
  • Nathan Phelps

    (University of Western Ontario, Financial Wellness Lab)

Abstract

When using clustering to uncover patterns in a dataset, a data analyst must make several decisions. In some cases, one of those decisions is how to handle binned data (e.g., age or income bands), which is a common data type collected in surveys. When clustering, it is possible to encode this variable as a nominal, ordinal, or interval-scaled variable (e.g., using the bin’s midpoint), and it is not clear which of these encodings, if any, should be preferred over others. We examined the impacts of these encodings on clustering results obtained from four clustering algorithms: partitioning around medoids (PAM) with Gower’s distance, K-prototypes, a latent class model, and KAMILA, on several simulated datasets and three household finance survey datasets from North America. We found that the optimal encoding varies depending on the clustering algorithm. We recommend the nominal encoding for latent class models, the ordinal encoding for K-prototypes and KAMILA (although the results were less definitive for these two), and the midpoint encoding for PAM.

Suggested Citation

  • Nathan Phelps, 2026. "Implications of Different Encodings of Binned Data when Clustering," Journal of Classification, Springer;The Classification Society, vol. 43(1), pages 146-174, April.
    • Handle: RePEc:spr:jclass:v:43:y:2026:i:1:d:10.1007_s00357-025-09522-5
    DOI: 10.1007/s00357-025-09522-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-025-09522-5
    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/s00357-025-09522-5?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    3. Yana Melnykov & Xuwen Zhu & Volodymyr Melnykov, 2021. "Transformation mixture modeling for skewed data groups with heavy tails and scatter," Computational Statistics, Springer, vol. 36(1), pages 61-78, March.
    4. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2021. "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions," JRFM, MDPI, vol. 14(2), pages 1-29, 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. Paula M. Murray & Ryan P. Browne & Paul D. McNicholas, 2020. "Mixtures of Hidden Truncation Hyperbolic Factor Analyzers," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 366-379, July.
    2. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.
    3. Nikola Počuča & Michael P. B. Gallaugher & Paul D. McNicholas, 2026. "Modelling Shanghai soil properties with finite mixtures of $$S_\text {U}$$ Johnson distributions," Computational Statistics, Springer, vol. 41(4), pages 1-21, June.
    4. Fulvia Pennoni & Francesco Bartolucci & Silvia Pandolfi, 2024. "Erratum to: Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 590-590, November.
    5. Alexa A. Sochaniwsky & Michael P. B. Gallaugher & Yang Tang & Paul D. McNicholas, 2025. "Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions," Journal of Classification, Springer;The Classification Society, vol. 42(1), pages 113-133, March.
    6. Yuan Fang & Dimitris Karlis & Sanjeena Subedi, 2022. "Infinite Mixtures of Multivariate Normal-Inverse Gaussian Distributions for Clustering of Skewed Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 510-552, November.
    7. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    8. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2017. "Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 141-156.
    9. Paul McLaughlin & Brian C. Franczak & Adam B. Kashlak, 2024. "Unsupervised Classification with a Family of Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures," Journal of Classification, Springer;The Classification Society, vol. 41(1), pages 65-93, March.
    10. Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2021. "Matrix Normal Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 556-575, October.
    11. Preeti & Kusum Deep, 2024. "Density Peak Clustering Using Grey Wolf Optimization Approach," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 338-370, July.
    12. Abby Flynt & Nema Dean & Rebecca Nugent, 2019. "sARI: a soft agreement measure for class partitions incorporating assignment probabilities," 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. 13(1), pages 303-323, March.
    13. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2017. "A mixture of SDB skew-t factor analyzers," Econometrics and Statistics, Elsevier, vol. 3(C), pages 160-168.
    14. Morris, Katherine & Punzo, Antonio & McNicholas, Paul D. & Browne, Ryan P., 2019. "Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 145-166.
    15. Cui, Xuetong & Murphy, Orla A. & McNicholas, Paul D., 2026. "Copula-based mixtures of regression models for multivariate response data," Computational Statistics & Data Analysis, Elsevier, vol. 218(C).
    16. Ryan P. Browne & Luca Bagnato & Antonio Punzo, 2024. "Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions," 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. 18(3), pages 597-625, September.
    17. Yingying Zhang & Volodymyr Melnykov & Igor Melnykov, 2023. "On Model-Based Clustering of Directional Data with Heavy Tails," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 527-551, November.
    18. Xuwen Zhu & Yana Melnykov, 2022. "On Finite Mixture Modeling of Change-point Processes," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 3-22, March.
    19. Wei, Yuhong & Tang, Yang & McNicholas, Paul D., 2019. "Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 18-41.
    20. Alex Sharp & Glen Chalatov & Ryan P. Browne, 2023. "A dual subspace parsimonious mixture of matrix normal distributions," 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 801-822, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:jclass:v:43:y:2026:i:1:d:10.1007_s00357-025-09522-5. 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.