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An ensemble model approach For many-feature data Clustering
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The ensemble is a popular machine learning technique based on the principle of divide and conquer. In data clustering, the ensemble aims to improve performance in terms of processing speed and clustering quality. Most existing ensemble methods face inherent complex challenges such as uncertainty, ambiguity, and overlap. Fuzzy clustering has recently been developed to handle data with many-feature, heterogeneity, uncertainty, and big data. In this paper, we propose an ensemble featurereduction cl
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Bài báo tạp chí
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