Parallel Spectral Clustering Export

@incollection{song08spectral, 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 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.}, author = {Song, Yangqiu and Chen, Wen-Yen and Bai, Hongjie and Lin, Chih-Jen and Chang, Edward}, citeulike-article-id = {5848878}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-87481-2_25}, citeulike-linkout-1 = {http://www.springerlink.com/content/w73378t717757154}, doi = {10.1007/978-3-540-87481-2_25}, journal = {Machine Learning and Knowledge Discovery in Databases}, keywords = {clustering, parallel, spectral}, pages = {374--389}, posted-at = {2009-09-29 11:19:29}, priority = {2}, title = {Parallel Spectral Clustering}, url = {http://dx.doi.org/10.1007/978-3-540-87481-2_25}, year = {2008} }

TY - CHAP ID - song08spectral L3 - citeulike-article-id:5848878 N2 - 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 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. JF - Machine Learning and Knowledge Discovery in Databases EP - 389 TI - Parallel Spectral Clustering SP - 374 KW - clustering KW - parallel KW - spectral AU - Song, Yangqiu AU - Chen, Wen-Yen AU - Bai, Hongjie AU - Lin, Chih-Jen AU - Chang, Edward PY - 2008/// UR - http://dx.doi.org/10.1007/978-3-540-87481-2_25 ER -