title: K-means tree VS randomized KD-tree?
body: <p>I was reading <a href="http://image.ntua.gr/iva/files/ann.pdf" rel="nofollow">these</a> slides. In particular, at slide 52 it's stated:</p>
  
  <blockquote>
    <p>In our experiments, we have found that either of two algorithms can
    have the best performance, depending on the dataset and desired
    precision</p>
  </blockquote>
  
  <p>However, in the previous slide only in case (a) K-means tree has a better performance than randomized kd-tree, while in the other three experiments kd-tree is definitely a winner.</p>
  
  <p>Is that correct or am I reading something wrong here? If so, why they say that the best algorithm is data-set-dependent?</p>