A method for evolving data streams

With recent advancement in technology need for analysis of such unbounded streams is increasing day by day. Data mining process helps to excavate useful knowledge from rapidly generated raw data streams. In context with the continuously generated data, mining data streams is emerging challenging task in which several issues like limited space, limited time, accuracy, handling evolving data need to be considered. In this paper the main method of research is clustering which is focused to handle evolving data streams. Most of the previously proposed methods inherit the drawbacks of k means method and fail to handle the issues. A hybrid data mining approach encompassing windowing, grid and density clustering and divide and merge method is proposed in this paper. A dynamic data stream clustering algorithm (DDS) is used in which a dynamic density threshold is designed to accommodate the changing density of grids with time in data stream. At last divide and merge approach is used to handle varying data points and further refine the quality of result obtained.