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• W1572186587 abstract "Ensemble clustering have been attracting lots of attentions, which combining several base data partitions to generate a single consensus partition with improved stability and robustness. Diversity is critical for the success of ensemble clustering. To enhance this characteristic, a subset of cluster ensemble is selected by removing the redundant partitions. Combined with ranking and forward selection strategies, the significance of attribute defined in rough set theory is employed as a heuristic to find the subset of cluster ensemble. Experimental results on the UCI machine learning repository demonstrate that the proposed algorithm is feasible and effective." @default.
• W1572186587 created "2016-06-24" @default.
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• W1572186587 date "2013-07-09" @default.
• W1572186587 modified "2023-09-26" @default.
• W1572186587 title "Rough set based cluster ensemble selection" @default.
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• W1572186587 hasPublicationYear "2013" @default.
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