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中文摘要：
聚类研究在数据挖掘研究领域中占有十分重要的地位．虽然目前已有很多数据聚类算法，但精度仍不够理想．文中提出一个基于结构化相似度的网络聚类算法（SSNCA），试图从网络聚类角度进一步提高数据聚类精度．具体解决方案是，将待聚类的向量数据集转化为k最近邻网络，并用SSNCA对该网络进行聚类．将SSNCA与c-Means、仿射传播进行比较，实验表明文中算法得到的目标函数稍差，但聚类精度要明显高于这两个算法．
英文摘要：
Data clustering is a hotspot in data mining area. Though there have been lots of data clustering algorithms now, the clustering accuracy of them is far from perfect. A structural similarity based network clustering algorithm （SSNCA） is proposed in this paper, which attempt to further improve the data clustering accuracy from the view of network clustering. The concrete solution scheme is that vector dataset for clustering is converted to a k-Nearest-Neigborhood network and SSNCA is used to cluster this network. Comparing SSNCA with the algorithms of c-Means and affinity propagation （AP）, experimental result shows that the fitness value got by the proposed algorithm is a little worse than AP, but its clustering accuracy is obviously better than that of the other two algorithms.
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