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中文摘要：
由于基于免疫的学习方法能够较好地适应数据流不断变化及高速处理的要求，本文据此提出一种基于免疫原理的数据流聚类算法（AIN—STREAM）．该算法能够动态适应数据流的变化，并能有效抑制噪声．AIN—STREAM通过建立与维护B细胞特征向量，从而能够根据用户的要求自动调整B细胞的识别区域，保证聚类结果的稳定性．理论分析和实验结果表明，在聚类结果相当的条件下，AIN—STREAM具有比同类算法更高的时间与空间效率，同时具有较高的聚类精度．
英文摘要：
The learning based on immune principle adapts well to the dynamic environment, and thus it can be applied to the data stream processing which is dynamic and requires high-speed processing. Therefore, an algorithm of clustering data streams based on immune principle is proposed, namely AIN-STREAM. The proposed algorithm can track the evolving clusters on noisy data sets. AIN-STREAM is capable of adjusting the recognition zone of B-cells automatically according to the requirement of users by creating and maintaining the B-Cell feature vectors. Thus, the stability of the clustering result is ensured. Theoretical analysis and comprehensive experimental results demonstrate that AIN-STREAM is superior over other immune principle based clustering algorithms under the circumstance of similar clustering results. Moreover, the results show that AIN-STREAM has a high clustering quality.
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