Parallel Spectral Clustering
Abstract
Spectral clustering algorithm has been shown to be more e ective in nding clusters than most traditional algorithms. However, spectral clustering su ers from a scalability problem in both memory use and computational time when a dataset size is large. To perform clustering 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 effectively alleviate the scalability problem.
Citation: Parallel Spectral Clustering, Yangqiu Song, Wen-Yen Chen, Hongjie Bai, Chih-Jen Lin, Edward Chang, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2008, pp. 374-389.
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