Parallel Spectral Clustering Yangqiu Song1,4, Wen-Yen Chen2,4, Hongjie Bai4,

Abstract. Spectral clustering algorithm has been shown to be more effective in finding clusters than most traditional algorithms. However, spectral clustering suffers from a scalability problem in both memory use and computational time when a dataset size is large. To perform clus-tering on large datasets, we propose to parallelize both memory use and computation on distributed computers. Through an empirical study on a large document dataset of 193, 844 data instances and a large photo dataset of 637, 137, we demonstrate that our parallel algorithm can ef-fectively alleviate the scalability problem. Key words: Parallel spectral clustering, distributed computing