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The traditional K-means algorithm is sensitive to the initial center, and equates the importance of dimension data for multidimensional data. So it is unable to block the effects of dimensional data dimension, nor can it well reflect the influence of each dimension of clustering. The semi-supervised clustering introduces a small amount of sample points, so that it can significantly reduce the number...
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