文件名称:statistics_kmeans
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- matlab例程
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- 上传时间：
- 2018-02-07
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- 2kb
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- 提 供 者：
- Brook******
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K-means算法是一种硬聚类算法，根据数据到聚类中心的某种距离来作为判别该数据所属类别。K-means算法以距离作为相似度测度。(kmeans uses the k-means++ algorithm for centroid initialization and squared Euclidean distance by default. It is good practice to search for lower, local minima by setting the 'Replicates' name-value pair argument.
idx is a vector of predicted cluster indices corrresponding to the observations in X. C is a 3-by-2 matrix containing the final centroid locations.
Use kmeans to compute the distance from each centroid to points on a grid. To do this, pass the centroids (C) and points on a grid to kmeans, and implement one iteration of the algorithm.)
idx is a vector of predicted cluster indices corrresponding to the observations in X. C is a 3-by-2 matrix containing the final centroid locations.
Use kmeans to compute the distance from each centroid to points on a grid. To do this, pass the centroids (C) and points on a grid to kmeans, and implement one iteration of the algorithm.)
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下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| statistics_kmeans.m | 9658 | 2017-06-26 |