- the incremental and distributed features, inspired by the adaptive networks protocols in "Ad-hoc" radiocommunications field, initiated by Mitton, Fleury [16]. 

## 2.2. Our Incremental Density-Based Algorithm

This algorithm is fully described and commented in Lelu [2] and Lelu, Cuxac [3]. The principles and pseudocode description are as follows: After a non-conventional normalization of the document input vector, we update a K-nearest neighbors similarity graph between the prior input vectors, whose valued edges are the cosine values between neighbors; to each node is attributed a density value, derived from the well-known clustering coefficient, and one or several clusterhead IDs. 

 The new input vector induces local perturbations in the density values, or "density landscape" of the graph, and thus in the clusterhead structure: it may create a new cluster, and be itself clusterhead, if it is denser than its neighbors; or it may link up with existing one(s) and alter the influence areas of the clusterheads, by means of our multiple inheritance rule - a document-vector inherits the clusterhead IDs from all the "overhanging", denser nodes in its 1-neighborhood. 

_______________________________________________________________________________________________________ 
. Initialization: the first node in the sequence has no link, has a 0 density and is its own clusterhead. LCC = Ø // LCC is the list of clusterhead lists for each node // 
. FOR each new node : 
 // induced changes for densities: // 
.compute its 1- et 2-neighbourhood (income and outcome), and other induced neighbourhood modifications, from which follows the list LL of the nodes concerned by a link creation / suppr. / modif. 

.compute its density from its 1-neighbourhood. 

.FOR each LL node, and any node in its 1- neighbourhood: 
- compute the new density value End FOR 
 // induced changes for clusterheads: // 
L = LL 
WHILE the list L of nodes akin to change their state is not empty : 
list LS = Ø FOR each L node, sorted by decreasing density : 
~ apply the rule for clusterhead change according to clusterheads of the inbound denser neighbors (in LCC) 
and their densities. 

~ if a change occurs: 
.update LCC for the current node .compute the possible overhanged nodes (overhanged by the current node); .increment the LS list of overhanged nodes. 

End FOR 
L=LS 
End WHILE 
 end FOR 
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The choice of our unique parameter K has proved to be a compromise between excessive fragmentation of the clusters (K=1) and excessive chaining effects (K>=4). 

## 2.3. Application To A 2-Years Evolution Of Geotechnical Literature

PASCAL is a general science bibliographic database edited by CNRS / INIST. We have extracted 2598 records in the field of geotechnics, from 2003 (1541 papers) to 2004 (1057 papers), described by a vocabulary of 3731 keywords, once eliminated frequent generic or off-topic terms as well as rare ones. Our GERMEN algorithm, with parameter K=3, created 179 kernels at the step 2003, 294 at the step 2004. Papers are distributed approximately as follows: 50% in the kernels, of size ranging from 2 to 35 