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• W2037337722 abstract "Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets." @default.
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• W2037337722 date "2013-07-01" @default.
• W2037337722 modified "2023-09-27" @default.
• W2037337722 title "Data weighing mechanisms for clustering ensembles" @default.
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• W2037337722 doi "https://doi.org/10.1016/j.compeleceng.2013.02.004" @default.
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