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- W2372227377 abstract "Usually,semi-supervised clustering algorithms utilize a small amount of labeled data to improve cluster parameters which guide the clustering of unlabeled data.However,the existing semi-supervised clustering algorithms(such as cluster centroid) ignore the labeled data could directly affect the clustering of unlabeled data.It proposes a double adjustment strategy which adjusts unlabeled data clustering with the labeled information,after the data is clustered according to the cluster parameters.Thus,the proposed method improves the clustering accuracy.The adjustment extension is changed dynamically by the local density around the labeled data.And a novel similarity measure is proposed to improve the accuracy of the adjusted unlabeled data.It modifies two algorithms,based on multinomial model semi-supervised clustering algorithm and semi-supervised fuzzy clustering algorithm,with the double adjustment method.Experimental results show that the method could improve the accuracy of semi-supervised clustering." @default.
- W2372227377 created "2016-06-24" @default.
- W2372227377 creator A5061211823 @default.
- W2372227377 date "2013-01-01" @default.
- W2372227377 modified "2023-09-26" @default.
- W2372227377 title "Semi-supervised Clustering Algorithm Based on Double Adjustable Strategy" @default.
- W2372227377 hasPublicationYear "2013" @default.
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