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Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule...
Datasets with missing values are frequent in clustering analysis. It seems obvious that the reconstruction of missing attribute values can be considered as the key factors impacting the clustering performance. For this, a FCM clustering algorithm for incomplete data sets based on human-computer cooperation is proposed in this paper. On account of the uncertainty of missing attributes, intervals are...
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