Rough Fuzzy Subspace Clustering for Data with Missing Values


  • Krzysztof Simiński Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice


Rough fuzzy subspace clustering, clustering, rough set, marginalisation, imputation, missing values


The paper presents rough fuzzy subspace clustering algorithm and experimental results of clustering. In this algorithm three approaches for handling missing values are used: marginalisation, imputation and rough sets. The algorithm also assigns weights to attributes in each cluster; this leads to subspace clustering. The parameters of clusters are elaborated in the iterative procedure based on minimising of criterion function. The crucial parameter of the proposed algorithm is the parameter having the influence on the sharpness of elaborated subspace cluster. The lower values of the parameter lead to selection of the most important attribute. The higher values create clusters in the global space, not in subspaces. The paper is accompanied by results of clustering of synthetic and real life data sets.


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How to Cite

Simiński, K. (2014). Rough Fuzzy Subspace Clustering for Data with Missing Values. COMPUTING AND INFORMATICS, 33(1), 131–153. Retrieved from