Classification dynamique d'un flux documentaire : une \'evaluation
  statique pr\'ealable de l'algorithme GERMEN
  Data-stream clustering is an ever-expanding subdomain of knowledge
extraction. Most of the past and present research effort aims at efficient
scaling up for the huge data repositories. Our approach focuses on qualitative
improvement, mainly for "weak signals" detection and precise tracking of
topical evolutions in the framework of information watch - though scalability
is intrinsically guaranteed in a possibly distributed implementation. Our
GERMEN algorithm exhaustively picks up the whole set of density peaks of the
data at time t, by identifying the local perturbations induced by the current
document vector, such as changing cluster borders, or new/vanishing clusters.
Optimality yields from the uniqueness 1) of the density landscape for any value
of our zoom parameter, 2) of the cluster allocation operated by our border
propagation rule. This results in a rigorous independence from the data
presentation ranking or any initialization parameter. We present here as a
first step the only assessment of a static view resulting from one year of the
CNRS/INIST Pascal database in the field of geotechnics.
