ns is obtained for the case \(p=2\) ( \(l^2\) norm), because in this case the optimal centers are the means of the input vectors assigned to them. Here the generalization \(p=1\) ( \(l^1\) norm) will also be considered.</p>
<p>Up to normalization, the K-means objective \(E\) is also the average reconstruction error if the original points are approximated with the cluster centers. Thus K-means is used not only to group the input points into cluster, but also to <code>quantize</code> their values.</p>
<p>K-me