An agent-based dual-tier algorithm for clustering data streams

Characteristics of data stream make it difficult for the clustering algorithms to satisfy the requirements on efficiency and effectiveness. This paper proposes a data stream clustering algorithm on dual-tier structure which employs the agent method. In the on-line process, a set of agents working simultaneously collect similar data points into sub-clusters by applying a heuristic strategy. And in the off-line process, summary information from the on-line component will be further analyzed to obtain the final clusters. The algorithm also supports the time-window queries on streams. The empirical evidence shows that this method can obtain high-quality clusters with low time complexity. analysis over an arbitrary period of the stream etc. As for stream clustering, a common method is dividing the streaming data into chunks, and algorithms for static sets can be used on each sub-set separately (2). In recent years, stream algorithms have developed into a two-phase structure (3), (4). Usually, a dual framework includes two parts: the on-line component and the off-line component. The former is responsible for the fast but rough processing of streaming data and saving the summary information to meet the one-pass restriction while the latter takes advantage of the information to conduct high-level analysis. At present, stream algorithms are still facing some problems, for example: sensitive to the initial data points; bad quality of clusters due to the loss of global information caused by dividing the stream; high time complexity etc. A novel dual-tier clustering algorithm for data streams, AGCluStream, is proposed in this paper. The on-line algorithm uses agents to make similar points denser in local areas, and record the temporary distribution of data according to the pyramidal time frame (3). The off-line algorithm uses these records to conduct time-window analysis and higher-level clustering analysis. AGCluStream dose not divide the stream, and it adopts an incomplete-partition strategy to maintain the global information more effectively.