which can be taken as the sum of the squared distances to the cluster centers, the sum of the squared error(SSE). We calculate the error of each data point (i.e., its distance to the closest 算法可以被观看作为一种贪婪的算法为partitioningnsamples入kclusters以便使anobjective作用,减到最小可...
awhere ci is the center of theith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting prototyp...
awhere ci is the center of theith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting prototyp...
awhereci is the center of theith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting ...