thm to approximately (and quickly) find the closest cluster center to each point. The KD-tree implementation is based on <a class="el" href="kdtree.html">KD-trees and forests</a>.</p>
<p>The algorithm can be summarized as follows:</p>
<ol type="1">
<li><b>Quantization.</b> Each point \(\bx_i\) is reassigned to the center \(\bc_{q_j}\) closer to it. This starts by indexing the \(K\) centers by a KD-tree and then using the latter to quickly find the closest center for every training point. The search is appro