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System for identifying clusters in scatter plots using smoothed polygons with optimal boundaries

机译:使用具有最佳边界的平滑多边形在散点图中识别群集的系统

摘要

An apparatus and method for identifying clusters in two-dimensional data by generating a two-dimensional histogram characterized by a grid of bins, determining a density estimate based on the bins, and identifying at least one cluster in the data. A smoothed density estimate is generated using a Gaussian kernel estimator algorithm. Clusters are identified by locating peaks and valleys in the density estimate (e.g., by comparing slope of adjacent bins). Boundaries (e.g., polygons) around clusters are identified using bins after bins are identified as being associated with a cluster. Boundaries can be simplified (e.g., by reducing the number of vertices in a polygon) to facilitate data manipulation.
机译:一种用于通过生成以箱体网格为特征的二维直方图,基于箱体确定密度估计并识别数据中的至少一个群集来识别二维数据中的群集的装置和方法。使用高斯核估计器算法生成平滑的密度估计。通过在密度估计中定位峰和谷(例如,通过比较相邻箱的斜率)来识别簇。在将条带识别为与簇相关联之后,使用条带识别簇周围的边界(例如,多边形)。边界可以简化(例如,通过减少多边形中的顶点数量)以促进数据操纵。

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