This article assumes you choose host001 as NN, host002 as JT, and host003 as SNN.

本文假设选择host001作为NN，host002 作为JT，host003 作为SNN。

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This paper presents a new method of text clustering by using the latent semantic index (LSI) and self-organizing neural network (SNN).

根据隐含语义索引（LSI）理论和动态自组织映射神经网络理论，提出了一种文本聚类的新方法。

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The results demonstrate that SNN algorithm is not sensitive to distance metrics and performs better at the classification accuracy on large data sets.

最后， 总结得出SNN分类算法对距离度量不敏感，且在大数据集上具有更好分类效果的结论。

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Based on the improved SNN similarity matrix and spectral bisection method, this paper proposed a new algorithm for detecting the community structure in complex networks.

基于改进的SNN相似度矩阵与谱平分法，提出了一种寻找复杂网络社团结构的算法。

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Conclusion Findings of both plain and dynamic enhanced MRI can correlate well with pathologic characteristics and are highly specific for diagnosis of SNN obviously superior to us and ct.

结论平扫加动态增强MRI能良好反映孤立性坏死结节的病理特征，其表现具高度的诊断特异性，明显优于超声和CT。

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Conclusion Findings of both plain and dynamic enhanced MRI can correlate well with pathologic characteristics and are highly specific for diagnosis of SNN obviously superior to us and ct.

结论平扫加动态增强MRI能良好反映孤立性坏死结节的病理特征，其表现具高度的诊断特异性，明显优于超声和CT。

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