Immune clone selection graph partition algorithm
免疫克隆选择图划分方法

In order to solve the problem of the storage and eigendecomposition of the similarity matrix in spectral clustering algorithms, this paper proposed a new method utilizing the immune clone selection optimizing algorithm to solve the graph partition. It utilized the equivalence of the graph partitioning and the weighted kernel K-means objectives and adopted the graph partitioning objective as the affinity function. Especially introduced an individual adjustment operator into the immune clone selection optimizing algorithm, which made the individual to evolve in better direction and higher speed. In addition, it introdced a novel distance measure to construct the similarity matrix, namely manifold distance measure, which made the method behave well in data sets with complex structure. The experimental results on six artificial datasets, the USPS handwritten digit datasets and UMIST face datasets show that the novel method is effective and robust.