Approximate pairwise clustering for large data sets via sampling plus extension

Liang Wang, Christopher Leckie, Ramamohanarao Kotagiri, James Bezdek
2011 Pattern Recognition  
Pairwise clustering methods have shown great promise for many real-world applications. However, the computational demands of these methods make them impractical for use with large data sets. The contribution of this paper is a simple but efficient method, called eSPEC, that makes clustering feasible for problems involving large data sets. Our solution adopts a "sampling, clustering plus extension" strategy. The methodology starts by selecting a small number of representative samples from the
more » ... ational pairwise data using a selective sampling scheme; then the chosen samples are grouped using a pairwise clustering algorithm combined with local scaling; and finally, the label assignments of the remaining instances in the data are extended as a classification problem in a low-dimensional space, which is explicitly learned from the labeled samples using a cluster-preserving graph embedding technique. Extensive experimental results on several synthetic and real-world data sets demonstrate both the feasibility of approximately clustering large data sets and acceleration of clustering in loadable data sets of our method.
doi:10.1016/j.patcog.2010.08.005 fatcat:b2azjgg25vg75fx5rvbqbmzmku