与三维网格模型的K均值聚类分割、点云模型的谱系聚类分割的实验结果比较证实了这一点。

This is supported by the comparison with the results of hierarchical clustering segmentation of point cloud model and K-Means clustering segmentation of mesh model.

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比较不同的系统聚类方法，类平均法能较好适合辣椒品种的数量分类。

Compared with other systematic clustering method, UPGMA would be better for the numerical taxonomy of the capsicum varieties.

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聚类通过比较数据的相似性和差异性，能发现数据的内在特征及分布规律，从而获得对数据更深刻的理解与认识。

By contrasting the similarity and dissimilarity in data, clustering can find out the data's inner characteristic and distribution rule, so we can obtain the further understanding.

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