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。
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。
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。
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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