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A widely used approach to clustering a single data stream is the two-phasedapproach in which the online phase creates and maintains micro-clusters whilethe off-line phase generates the macro-clustering from the micro-clusters. Weuse this approach to propose a distributed framework for clustering streamingdata. Our proposed framework consists of fundamen- tal processes: onecoordinator-site process and many remote-site processes. Remote-site processescan directly communicate with the coordinator-process but cannot communicatethe other remote site processes. Every remote-site process generates andmaintains micro- clusters that represent cluster information summary, from itslocal data stream. Remote sites send the local micro-clusterings to thecoordinator by the serialization technique, or the coordinator invokes theremote methods in order to get the local micro-clusterings from the remotesites. After the coordinator receives all the local micro-clusterings from theremote sites, it generates the global clustering by the macro-clusteringmethod. Our theoretical and empirical results show that, the global clusteringgenerated by our distributed framework is similar to the clustering generatedby the underlying centralized algorithm on the same data set. By using thelocal micro-clustering approach, our framework achieves high scalability, andcommunication-efficiency.

Communication-Efficient and Exact Clustering Distributed Streaming Data