Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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Applied Intelligence
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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.