  Due to the progressive growth of the amount of data available in a wide
variety of scientific fields, it has become more difficult to ma- nipulate and
analyze such information. Even though datasets have grown in size, the K-means
algorithm remains as one of the most popular clustering methods, in spite of
its dependency on the initial settings and high computational cost, especially
in terms of distance computations. In this work, we propose an efficient
approximation to the K-means problem intended for massive data. Our approach
recursively partitions the entire dataset into a small number of sub- sets,
each of which is characterized by its representative (center of mass) and
weight (cardinality), afterwards a weighted version of the K-means algorithm is
applied over such local representation, which can drastically reduce the number
of distances computed. In addition to some theoretical properties, experimental
results indicate that our method outperforms well-known approaches, such as the
K-means++ and the minibatch K-means, in terms of the relation between number of
distance computations and the quality of the approximation.
