  Clustering with fast algorithms large samples of high dimensional data is an
important challenge in computational statistics. Borrowing ideas from MacQueen
(1967) who introduced a sequential version of the $k$-means algorithm, a new
class of recursive stochastic gradient algorithms designed for the $k$-medians
loss criterion is proposed. By their recursive nature, these algorithms are
very fast and are well adapted to deal with large samples of data that are
allowed to arrive sequentially. It is proved that the stochastic gradient
algorithm converges almost surely to the set of stationary points of the
underlying loss criterion. A particular attention is paid to the averaged
versions, which are known to have better performances, and a data-driven
procedure that allows automatic selection of the value of the descent step is
proposed.
  The performance of the averaged sequential estimator is compared on a
simulation study, both in terms of computation speed and accuracy of the
estimations, with more classical partitioning techniques such as $k$-means,
trimmed $k$-means and PAM (partitioning around medoids). Finally, this new
online clustering technique is illustrated on determining television audience
profiles with a sample of more than 5000 individual television audiences
measured every minute over a period of 24 hours.
