| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 100 === Clustering multi-dimensional data stream is a difficult and important problem. The goal is to cluster the objects within the stream continuously, to discover and monitor the evolving up-to-dated events. Density grid based clustering algorithms are fast, and can discover arbitrarily shaped clusters and deal with noise. However, the sizes and borders of the grids easily influence Grid-based algorithms. We propose a Dynamic Grid-Based Clustering algorithm for high-dimensional data streams. When new data arrives, the grid structure is dynamically updated. Dynamic grid structures adjust its range and boundary on each dimension over time to produce effective clustering results with low memory usage. We used both synthetic and real data set for experiments, and the experimental results show that our proposed algorithm has superior quality and efficiency, can find clusters of arbitrary shapes, and can accurately recognize the evolving behaviors of real-time data stream
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