Document stream clustering: experimenting an incremental algorithm and
  AR-based tools for highlighting dynamic trends
  We address here two major challenges presented by dynamic data mining: 1) the
stability challenge: we have implemented a rigorous incremental density-based
clustering algorithm, independent from any initial conditions and ordering of
the data-vectors stream, 2) the cognitive challenge: we have implemented a
stringent selection process of association rules between clusters at time t-1
and time t for directly generating the main conclusions about the dynamics of a
data-stream. We illustrate these points with an application to a two years and
2600 documents scientific information database.
