Multilevel Clustering Algorithm Using Core-Sets Coarsening
Runing Ma · Jisuanji kexue yu tansuo · 2013
Coarsening phase is the most critical step among procedures in multilevel clustering algorithm. Some classi cal multilevel clustering algorithms, such as METIS (multilevel scheme for partitioning irregular graphs) and Graclus, use some criterions of vertex and edge weights to capture the collapsing of the vertex and edges and realize coarsening procedure. But there is the disadvantage that the coarsest dataset can not formulate the global information and struc ture of original dataset correctly. This paper proposes a core-sets coarsening method, which defines multilevel coresets to retain global information of layered dataset in perspective. Meanwhile, as the coarsest dataset has the same num ber as clustering, and each core point corresponds to a single class, the partitioning procedure need not be considered. Some numerical experiments verify the superiority and availability of the proposed algorithm.