In order to explore the real features of outcome clusters, this paper proposed a suborbicular cluster recognition algorithm based on multi-dimensional analysis. Firstly, it analyzed the given data sets in each dimension, then drew the frequency cure of every dimension and compared the characteristics of those cures. Finally, it identified the suborbicular cluster from a number of clusters. A large number of experiments show that the algorithm is robust, effective. It can be extended to high-dimensional data sets.%为了深入地探索聚类结果簇的形态特征,提出了一种基于维度映射的类圆簇识别算法.该算法将结果簇按维度进行映射,通过比较、分析簇在各个映射维度上的频数曲线及形态特征,自动将类圆簇从众多结构复杂的聚类结果簇中识别出来.算法经过大量实验验证,具有很好的识别能力和抗干扰能力,对于高维度数据集合也具有很强的扩展性.
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