A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Approximate pairwise clustering for large data sets via sampling plus extension
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
doi:10.1016/j.patcog.2010.08.005
fatcat:b2azjgg25vg75fx5rvbqbmzmku