Abstract: After analyzing the classification based on the represent points as the results of clustering algorithm with uniform granularity and distance function，the problem of this classification caused by that clustering result is inconsistent with a prior knowledge is pointed out.In order to solve this problem and improve the classification accuracy and the generalized ability，an immune clustering algorithm used different granularities is presented in this section.Experimental results show that the new method overcomes the problem to some extends，and this method has good results in intrusion detection compared with RBF-ANN and BP-ANN.

摘要： 分析了基于均匀粒度的聚类方法构造分类器存在着与先验知识之间不协调的问题。提出了根据多粒度原理、基于人工免疫聚类来获取代表点集来构造分类器的方法，在一定程度上克服了聚类结果与先验知识之间的矛盾，并提高了分类器的分类准确度和推广性。实验结果表明基于此分类器的入侵检测的平均检测率和误报率都保持了较高的性能。