Qing Liu
Abstract
With strong ability for discovering arbitrary shape clusters and handling noise, grid-based data stream clustering algorithm efficiently resolves these problem of being very sensitive to the user-defined parameters and difficult to distinguish the density distinction of clusters.
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With strong ability for discovering arbitrary shape clusters and handling noise, grid-based data stream clustering algorithm efficiently resolves these problem of being very sensitive to the user-defined parameters and difficult to distinguish the density distinction of clusters.
Key concepts: Computer science, Cluster analysis, Data stream clustering, Grid, Data mining, Noise (video), Algorithm, Data stream