Imputation Strategies for Clustering Mixed-Type Data with Missing Values

Rabea Aschenbruck (), Gero Szepannek () and Adalbert F. X. Wilhelm ()
Additional contact information
Rabea Aschenbruck: Hochschule Stralsund - University of Applied Sciences
Gero Szepannek: Hochschule Stralsund - University of Applied Sciences
Adalbert F. X. Wilhelm: Jacobs University Bremen

Journal of Classification, 2023, vol. 40, issue 1, No 2, 2-24

Abstract: 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.

Keywords: Clustering; Imputation; Mixed-type data; Missing values (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00357-022-09422-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:40:y:2023:i:1:d:10.1007_s00357-022-09422-y

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-022-09422-y

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().