Fast approximate spectral clustering

Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-scale problems due to its computational complexity of O(n3 ) in general, with n the number of data points. We extend the range of spectral clustering by developing a general framework for fast approximate spectral clustering in which a distortion-minimizing local transformation is first applied to the data. This framework is based on a theoretical analysis that provides a statistical characterization of the effect of local distortion on the mis-clustering rate. We develop two concrete instances of our general framework, one based on local k-means clustering (KASP) and one based on random projection trees (RASP). Extensive experiments show that these algorithms can achieve significant speedups with little degradation in clustering accuracy. Specifically, our algorithms outperform k-means by a large margin in te...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Donghui Yan, Ling Huang, Michael I. Jordan
• Keywords: Approximate Spectral Clustering, Data Mining, KDD 2009, Spectral Cluster Data, Spectral Clustering Refers