The experimental results show that, the method is effective in clustering while dealing with undefined boundary problems, and is powerful in avoiding noise.

实验结果表明，该算法对聚类边界不清晰的数据集可获得较精确的聚类划分，同时具有很强的噪声抑制能力。

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In addition, by using the hole edges conditions, the relationship between undefined coefficients is set up, to reduce the number of those coefficients, and only the boundary collocation is needed.

同时，利用内孔边界条件，建立待定系数间的关系式，以减少待定系数的数目，并且只需要在外边界配点。

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Artificial immune network clustering is often ineffective when there is noise or undefined cluster boundary in the data.

当数据集聚类边界不清晰或存在噪声干扰时，人工免疫网络聚类算法通常无法获得有效的聚类划分。

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