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We propose a robust K-means clustering algorithm for document clustering, where we suppose that a document-term matrix is given as an input dataset, and the documents in the dataset are clustered on t...he basis of the frequency of terms that occur in each document. We introduce a robust loss function to K-means clustering to obtain its robust version, and also propose a feature transform method for improving the performance of document clustering. Experimental results show that the proposed method improves the robustness of K-means to outliers and the performance of document clustering demonstrated on one of the BBC datasets originating from the BBC News.続きを見る
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Mendeley出力
211-1439-2-PB