  Efficient extraction of useful knowledge from these data is still a
challenge, mainly when the data is distributed, heterogeneous and of different
quality depending on its corresponding local infrastructure. To reduce the
overhead cost, most of the existing distributed clustering approaches generate
global models by aggregating local results obtained on each individual node.
The complexity and quality of solutions depend highly on the quality of the
aggregation. In this respect, we proposed for distributed density-based
clustering that both reduces the communication overheads due to the data
exchange and improves the quality of the global models by considering the
shapes of local clusters. From preliminary results we show that this algorithm
is very promising.
