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Imputation Strategies for Clustering Mixed-Type Data with Missing Values

Author

Listed:
  • Rabea Aschenbruck

    (Hochschule Stralsund - University of Applied Sciences)

  • Gero Szepannek

    (Hochschule Stralsund - University of Applied Sciences)

  • Adalbert F. X. Wilhelm

    (Jacobs University Bremen)

Abstract

Incomplete data sets with different data types are difficult to handle, but regularly to be found in practical clustering tasks. Therefore in this paper, two procedures for clustering mixed-type data with missing values are derived and analyzed in a simulation study with respect to the factors of partition, prototypes, imputed values, and cluster assignment. Both approaches are based on the k-prototypes algorithm (an extension of k-means), which is one of the most common clustering methods for mixed-type data (i.e., numerical and categorical variables). For k-means clustering of incomplete data, the k-POD algorithm recently has been proposed, which imputes the missings with values of the associated cluster center. We derive an adaptation of the latter and additionally present a cluster aggregation strategy after multiple imputation. It turns out that even a simplified and time-saving variant of the presented method can compete with multiple imputation and subsequent pooling.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:1:d:10.1007_s00357-022-09422-y
    DOI: 10.1007/s00357-022-09422-y
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    References listed on IDEAS

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