基于流形距离的人工免疫半监督聚类算法

将流形距离作为样本间相似性的基本度量测度，加入成对约束信息，通过近部传播得出新的度量矩阵。把聚 类问题转化为一优化数学模型。采用克隆选择算法求解这个优化模型，得出最后的聚类结果，通过人工数据集和UCI 标准数据集验证了这种方法具有较高的准确性。

Artificial Immune Clustering Semi-supervised Algorithm Based on Manifold Distance

Manifold distance was used as the basic measure of the sample similarity between samples. The pair-wise constrains prior information was introduced,then the measure matrix was obtained through affinity propagation. So the clustering problem was transformed as one optimal model. Clonal selection algorithm was employed to solve this model, and the clustering results were given. Experiments on artificial data sets and UCI benchmark data set show that the pro- posed method can give the better accuracy.