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Visual Clustering and Boundary Detection of Time-Dependent Datasets

Visual clustering should be one of the basic tools for time-dependent data analysis in cyberworlds. This paper describes a novel approach to spatial clustering and boundary detection based on geometric modeling and visualization. Datasets and boundaries of clusters are visualized as 3D points and surfaces of reconstructed solids changing over time. Our approach applies the concepts of geometric solid modeling and uses density as clustering criteria that comes from traditional density-based clustering techniques. Visual clustering allows the user to analyze results of clustering the data changing over time and to interactively choose appropriate parameters.