  Distributed data mining techniques and mainly distributed clustering are
widely used in the last decade because they deal with very large and
heterogeneous datasets which cannot be gathered centrally. Current distributed
clustering approaches are normally generating global models by aggregating
local results that are obtained on each site. While this approach mines the
datasets on their locations the aggregation phase is complex, which may produce
incorrect and ambiguous global clusters and therefore incorrect knowledge. In
this paper we propose a new clustering approach for very large spatial datasets
that are heterogeneous and distributed. The approach is based on K-means
Algorithm but it generates the number of global clusters dynamically. Moreover,
this approach uses an elaborated aggregation phase. The aggregation phase is
designed in such a way that the overall process is efficient in time and memory
allocation. Preliminary results show that the proposed approach produces high
quality results and scales up well. We also compared it to two popular
clustering algorithms and show that this approach is much more efficient.
