  In this paper we propose a mixture model, SparseMix, for clustering of sparse
high dimensional binary data, which connects model-based with centroid-based
clustering. Every group is described by a representative and a probability
distribution modeling dispersion from this representative. In contrast to
classical mixture models based on EM algorithm, SparseMix:
  -is especially designed for the processing of sparse data,
  -can be efficiently realized by an on-line Hartigan optimization algorithm,
  -is able to automatically reduce unnecessary clusters.
  We perform extensive experimental studies on various types of data, which
confirm that SparseMix builds partitions with higher compatibility with
reference grouping than related methods. Moreover, constructed representatives
often better reveal the internal structure of data.
