document: Density-based clustering for real-time stream data Existing data-stream clustering algorithms such as CluStream arebased on k-means . These clustering algorithms are incompetent tofind clusters of arbitrary shapes and can not handle outliers . Further , they require the knowledge of k and user-specified time window . To address these issues , this paper proposes D-Stream , a framework for clustering stream data using adensity-based approach . The algorithm uses an online component which maps each input data record into a grid and an offline component which computes the grid density and clusters the grids based on the density . The algorithm adopts a density decaying technique to capture the dynamic changes of a data stream . Exploiting the intricate relationships between the decay factor , data density and cluster structure , our algorithm can efficiently and effectively generate and adjust the clusters in real time . Further , a theoretically sound technique is developed to detect and remove sporadic grids mapped to by outliers in order to dramatically improve the space and time efficiency of the system . The technique makes high-speed data stream clustering feasible without degrading the clustering quality . The experimental results show that our algorithm has superior quality and efficiency , can find clusters of arbitrary shapes , and can accurately recognize the evolving behaviors of real-time data streams .
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