A fast incremental spectral clustering for large data sets

Abstract

Spectral clustering is an emerging research topic that has numerous applications, such as data dimension reduction and image segmentation. In spectral clustering, as new data points are added continuously, dynamic data sets are processed in an on-line way to avoid costly re-computation. In this paper, we propose a new representative measure to compress the original data sets and maintain a set of representative points by continuously updating Eigen-system with the incidence vector. According to these extracted points we generate instant cluster labels as new data points arrive. Our method is effective and able to process large data sets due to its low time complexity. Experimental results over various real evolutional data sets show that our method provides fast and relatively accurate results.