K-means clustering MATLAB
2016-08-23
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K-means clustering method is divided into the following steps: first, initializing cluster centers 1, depending on the issue, based on experience from the sample set c-an appropriate sample was selected as the initial cluster centers. 2, c before using samples as the initial cluster centers. 3, all the samples were randomly divided into class c, calculate the sample mean of each type, the sample mean as the initial cluster centers. Second, the initial cluster 1, on principle samples into the nearest cluster Center is represented by the class. 2, take a sample, be classified in its closest cluster Center category, recalculate the sample mean, updating cluster centers. Then take a sample, repeat the operation until all the samples into the corresponding classes in the. Third, determine whether clustering reasonably squared error function to determine whether clustering is reasonable, unreasonable modification classification. Algorithm for judgment, modification cycle until it reaches the
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