  This paper proposes the use of an optimization algorithm, namely PSO to
decide the initial centroids in K-means, to eventually get better accuracy. The
vectorized notation of the optimal centroids can be thought of as entities in
an optimization space, where the accuracy of K-means over a random subset of
the data could act as a fitness measure. The resultant optimal vector can be
used as the initial centroids for K-means.

Machine Learning;Neural and Evolutionary Computing;Machine Learning