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- W2031805499 abstract "Semi-supervised clustering ensemble fully integrates the advantages of semi-supervised learning, clustering analysis and ensemble learning, as well as improves the performance of clustering. There are many works on the algorithm and the consensus function of semi-supervised clustering ensemble, but there are few studies in the theoretical analysis. In this paper, we analyze the convergence of semi-supervised clustering ensemble, and propose a new relabeling approach for semi-supervised clustering ensemble by majority voting. We prove that semi-supervised clustering ensemble is able to boost weak learners to strong learners which can make very accurate predictions. The experimental results on standard data sets show that the semi-supervised clustering ensemble has better performance." @default.
- W2031805499 created "2016-06-24" @default.
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- W2031805499 date "2013-03-01" @default.
- W2031805499 modified "2023-09-25" @default.
- W2031805499 title "Convergence analysis of semi-supervised clustering ensemble" @default.
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- W2031805499 doi "https://doi.org/10.1109/icist.2013.6747660" @default.
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