  In this paper, we compare three initialization schemes for the KMEANS
clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and
3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k
needs to be set by the user of the algorithms. (Kang 2013) recently proposed a
novel use of determinantal point processes for sampling the initial centroids
for the KMEANS algorithm (we call it KMEANSD++). They, however, do not provide
any evaluation establishing that KMEANSD++ is better than other algorithms. In
this paper, we show that the performance of KMEANSD++ is comparable to KMEANS++
(both of which are better than KMEANSRAND) with KMEANSD++ having an additional
that it can automatically approximate the value of k.
