  A widely used approach to clustering a single data stream is the two-phased
approach in which the online phase creates and maintains micro-clusters while
the off-line phase generates the macro-clustering from the micro-clusters. We
use this approach to propose a distributed framework for clustering streaming
data. Our proposed framework consists of fundamen- tal processes: one
coordinator-site process and many remote-site processes. Remote-site processes
can directly communicate with the coordinator-process but cannot communicate
the other remote site processes. Every remote-site process generates and
maintains micro- clusters that represent cluster information summary, from its
local data stream. Remote sites send the local micro-clusterings to the
coordinator by the serialization technique, or the coordinator invokes the
remote methods in order to get the local micro-clusterings from the remote
sites. After the coordinator receives all the local micro-clusterings from the
remote sites, it generates the global clustering by the macro-clustering
method. Our theoretical and empirical results show that, the global clustering
generated by our distributed framework is similar to the clustering generated
by the underlying centralized algorithm on the same data set. By using the
local micro-clustering approach, our framework achieves high scalability, and
communication-efficiency.
