from the input data and use them as initial values for the cluster centers.</p>
<dl class="section user"><dt>K-means++</dt><dd></dd></dl>
<p><a class="el" href="citelist.html#CITEREF_arthur07k-means">[3]</a> proposes a randomized initialization of the centers which improves upon random selection. The first center \(\bc_1\) is selected at random from the data points \(\bx_1, \dots, \bx_n \) and the distance from this center to all points \(\|\bx_i - \bc_1\|_p^p\) is computed. Then the second center \(\bc_2\)