Clustering with Missing Values

Article type: Research Article

Authors: Simiński, Krzysztof

Affiliations: Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland. [email protected]

Note: [] Address for correspondence: Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

Abstract: The paper presents the clustering algorithm for data with missing values. In this approach both marginalisation and imputation are applied. The result of the clustering is the type-2 fuzzy set / rough fuzzy set. This approach enables the distinction between original and imputed data. The method can be applied to the data sets with all attributes lacking some values. The paper is accompanied by the numerical examples of clustering of synthetic and real-life data sets.

Keywords: missing values, clustering

DOI: 10.3233/FI-2013-814

Journal: Fundamenta Informaticae, vol. 123, no. 3, pp. 331-350, 2013