Fast Immune Greedy Spectral Clustering - Gou, S P; Zhang, J; Jiao, L C; Yang, J Y (International Information Institute (Tokyo). Information, 2012)
The performance of the classical spectral clustering algorithm has bottleneck problem of requiring a great deal computation memory and computation time, which makes spectral clustering method limited in large-scale dataset application. To solve this, a novel algorithm called Immune Greedy Spectral Clustering (IGSC) algorithm is proposed, in which, the clonal selection algorithm is introduced to select the typical sample sets without any prior knowledge and to decrease the computational complexity of similarity matrix for eigenvector. The proposed algorithm substitutes a subset for all datasets to eigenvector decompose, which is regard as a combinatorial optimization issue in immune clonal selection algorithm. And immune clonal selection algorithm ensures a fast convergence to the global optimal solution so that the IGSC algorithm can achieve more stable and better clustering result The experiment results on UCI datasets, texture images show that IGSC algorithm needs no prior knowledge and saves much more time compared with Greedy Spectral Clustering (GSC) algorithm while also gets a better accuracy rate compared with the standard Nyström Spectral Clustering (NSC) algorithm. [PUBLICATION ABSTRACT]