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中文摘要：
根据K近邻、共享K近邻和互K近邻三种近邻算法的思想分别构造复杂网络，然后通过复杂网络的社团发现算法来实现对样本的聚类。最后，将三种方法分别在人工构造的非凸类簇数据集和UCI数据集上进行仿真实验，结果表明三种方法都是可行的，且互K近邻网络聚类方法还具有识别一定数量孤立点功能。
英文摘要：
This paper first built complex networks based on three algorithms of nearest neighbor .The al-gorithms were K-nearest neighbor , shared K-nearest neighbors and mutual K -nearest neighbor respectively . Then algorithms of community detection was used to achieve samples clustering .Finally, simulations on datasets of non-convex shape clusters and UCI were carried out based on the above three methods .The experimental re-sults show that three methods are all feasible , and the method of Mutual K -nearest neighbor can recognize some isolated points .
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