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So far most of the K-means algorithms use the number of the labeled data as the K value, but sometimes it doesnpsilat work well. In this paper, we propose a semi-supervised K-means algorithm based on the global optimization. It can select an appropriate number of clusters as the K value directly and plan a great amount of supervision data by using only a small amount of the labeled data. Combining...
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