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k -POD: A Method for k -Means Clustering of Missing Data

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  • Jocelyn T. Chi
  • Eric C. Chi
  • Richard G. Baraniuk

Abstract

The k -means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k -POD method presents a simple extension of k -means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.[Received November 2014. Revised August 2015.]

Suggested Citation

  • Jocelyn T. Chi & Eric C. Chi & Richard G. Baraniuk, 2016. "k -POD: A Method for k -Means Clustering of Missing Data," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 91-99, February.
    • Handle: RePEc:taf:amstat:v:70:y:2016:i:1:p:91-99
    DOI: 10.1080/00031305.2015.1086685
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    Cited by:

    1. Aleix Alcacer & Irene Epifanio & Jorge Valero & Alfredo Ballester, 2021. "Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size," Mathematics, MDPI, vol. 9(7), pages 1-15, April.
    2. 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.
    3. Lorenz Riess & Mathias Beiglbock & Johannes Temme & Andreas Wolf & Julio Backhoff, 2023. "The geometry of financial institutions -- Wasserstein clustering of financial data," Papers 2305.03565, arXiv.org.
    4. Rabea Aschenbruck & Gero Szepannek & Adalbert F. X. Wilhelm, 2023. "Imputation Strategies for Clustering Mixed-Type Data with Missing Values," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 2-24, April.

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